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OctoSense dataset

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  1. .gitattributes +70 -0
  2. README.md +284 -0
  3. assets/cal_axis.png +3 -0
  4. assets/gt.png +3 -0
  5. assets/hero.gif +3 -0
  6. calibrations/cam_cals/cam_cal_rosbag2_2026_01_04-16_13_08/calibration-camchain.yaml +47 -0
  7. calibrations/cam_cals/cam_cal_rosbag2_2026_01_04-16_13_08/calibration-results-cam.txt +50 -0
  8. calibrations/cam_cals/cam_cal_rosbag2_2026_01_04-16_13_08/cam_cal_rosbag2_2026_01_04-16_13_08.h5 +3 -0
  9. calibrations/cam_cals/cam_cal_rosbag2_2026_01_04-16_13_08/img_infrared.mp4 +3 -0
  10. calibrations/cam_cals/cam_cal_rosbag2_2026_01_04-16_13_08/img_left.mp4 +3 -0
  11. calibrations/cam_cals/cam_cal_rosbag2_2026_01_04-16_13_08/img_right.mp4 +3 -0
  12. calibrations/cam_cals/cam_cal_rosbag2_2026_01_04-16_13_08/ir_calib_result.json +252 -0
  13. calibrations/cam_cals/cam_cal_rosbag2_2026_01_04-16_13_08/lidar_calibration_results.yaml +22 -0
  14. calibrations/cam_cals/cam_cal_rosbag2_2026_01_04-16_13_08/time_offset.npz +3 -0
  15. calibrations/cam_cals/cam_cal_rosbag2_2026_01_08-15_24_20/calibration-camchain.yaml +47 -0
  16. calibrations/cam_cals/cam_cal_rosbag2_2026_01_08-15_24_20/calibration-results-cam.txt +50 -0
  17. calibrations/cam_cals/cam_cal_rosbag2_2026_01_08-15_24_20/cam_cal_rosbag2_2026_01_08-15_24_20.h5 +3 -0
  18. calibrations/cam_cals/cam_cal_rosbag2_2026_01_08-15_24_20/img_infrared.mp4 +3 -0
  19. calibrations/cam_cals/cam_cal_rosbag2_2026_01_08-15_24_20/img_left.mp4 +3 -0
  20. calibrations/cam_cals/cam_cal_rosbag2_2026_01_08-15_24_20/img_right.mp4 +3 -0
  21. calibrations/cam_cals/cam_cal_rosbag2_2026_01_08-15_24_20/ir_calib_result.json +232 -0
  22. calibrations/cam_cals/cam_cal_rosbag2_2026_01_08-15_24_20/lidar_calibration_results.yaml +22 -0
  23. calibrations/cam_cals/cam_cal_rosbag2_2026_01_08-15_24_20/time_offset.npz +3 -0
  24. calibrations/cam_cals/cam_cal_rosbag2_2026_01_14-08_47_13/calibration-camchain.yaml +47 -0
  25. calibrations/cam_cals/cam_cal_rosbag2_2026_01_14-08_47_13/calibration-results-cam.txt +50 -0
  26. calibrations/cam_cals/cam_cal_rosbag2_2026_01_14-08_47_13/cam_cal_rosbag2_2026_01_14-08_47_13.h5 +3 -0
  27. calibrations/cam_cals/cam_cal_rosbag2_2026_01_14-08_47_13/img_infrared.mp4 +3 -0
  28. calibrations/cam_cals/cam_cal_rosbag2_2026_01_14-08_47_13/img_left.mp4 +3 -0
  29. calibrations/cam_cals/cam_cal_rosbag2_2026_01_14-08_47_13/img_right.mp4 +3 -0
  30. calibrations/cam_cals/cam_cal_rosbag2_2026_01_14-08_47_13/ir_calib_result.json +200 -0
  31. calibrations/cam_cals/cam_cal_rosbag2_2026_01_14-08_47_13/lidar_calibration_results.yaml +22 -0
  32. calibrations/cam_cals/cam_cal_rosbag2_2026_01_14-08_47_13/time_offset.npz +3 -0
  33. calibrations/cam_cals/cam_cal_rosbag2_2026_01_20-16_31_50/calibration-camchain.yaml +47 -0
  34. calibrations/cam_cals/cam_cal_rosbag2_2026_01_20-16_31_50/calibration-results-cam.txt +50 -0
  35. calibrations/cam_cals/cam_cal_rosbag2_2026_01_20-16_31_50/cam_cal_rosbag2_2026_01_20-16_31_50.h5 +3 -0
  36. calibrations/cam_cals/cam_cal_rosbag2_2026_01_20-16_31_50/img_infrared.mp4 +3 -0
  37. calibrations/cam_cals/cam_cal_rosbag2_2026_01_20-16_31_50/img_left.mp4 +3 -0
  38. calibrations/cam_cals/cam_cal_rosbag2_2026_01_20-16_31_50/img_right.mp4 +3 -0
  39. calibrations/cam_cals/cam_cal_rosbag2_2026_01_20-16_31_50/ir_calib_result.json +244 -0
  40. calibrations/cam_cals/cam_cal_rosbag2_2026_01_20-16_31_50/lidar_calibration_results.yaml +22 -0
  41. calibrations/cam_cals/cam_cal_rosbag2_2026_01_20-16_31_50/time_offset.npz +3 -0
  42. calibrations/cam_cals/cam_cal_rosbag2_2026_02_01-08_50_41/calibration-camchain.yaml +47 -0
  43. calibrations/cam_cals/cam_cal_rosbag2_2026_02_01-08_50_41/calibration-results-cam.txt +50 -0
  44. calibrations/cam_cals/cam_cal_rosbag2_2026_02_01-08_50_41/cam_cal_rosbag2_2026_02_01-08_50_41.h5 +3 -0
  45. calibrations/cam_cals/cam_cal_rosbag2_2026_02_01-08_50_41/img_infrared.mp4 +3 -0
  46. calibrations/cam_cals/cam_cal_rosbag2_2026_02_01-08_50_41/img_left.mp4 +3 -0
  47. calibrations/cam_cals/cam_cal_rosbag2_2026_02_01-08_50_41/img_right.mp4 +3 -0
  48. calibrations/cam_cals/cam_cal_rosbag2_2026_02_01-08_50_41/ir_calib_result.json +232 -0
  49. calibrations/cam_cals/cam_cal_rosbag2_2026_02_01-08_50_41/lidar_calibration_results.yaml +22 -0
  50. calibrations/cam_cals/cam_cal_rosbag2_2026_02_01-08_50_41/time_offset.npz +3 -0
.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.avro filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ckpt filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.lz4 filter=lfs diff=lfs merge=lfs -text
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+ *.mds filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.npy filter=lfs diff=lfs merge=lfs -text
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+ *.npz filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tar filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.wasm filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zst filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - uncompressed
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+ *.pcm filter=lfs diff=lfs merge=lfs -text
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+ *.sam filter=lfs diff=lfs merge=lfs -text
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+ *.raw filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - compressed
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+ *.aac filter=lfs diff=lfs merge=lfs -text
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+ *.flac filter=lfs diff=lfs merge=lfs -text
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+ *.mp3 filter=lfs diff=lfs merge=lfs -text
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+ *.ogg filter=lfs diff=lfs merge=lfs -text
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+ *.wav filter=lfs diff=lfs merge=lfs -text
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+ # Image files - uncompressed
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+ *.bmp filter=lfs diff=lfs merge=lfs -text
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+ *.gif filter=lfs diff=lfs merge=lfs -text
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+ *.png filter=lfs diff=lfs merge=lfs -text
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+ *.tiff filter=lfs diff=lfs merge=lfs -text
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+ # Image files - compressed
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+ *.jpg filter=lfs diff=lfs merge=lfs -text
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+ *.jpeg filter=lfs diff=lfs merge=lfs -text
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+ *.webp filter=lfs diff=lfs merge=lfs -text
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+ # Video files - compressed
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+ *.mp4 filter=lfs diff=lfs merge=lfs -text
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+ *.webm filter=lfs diff=lfs merge=lfs -text
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+ calibrations/cam_cals/cam_cal_rosbag2_2026_01_04-16_13_08/calibration-report-cam.pdf filter=lfs diff=lfs merge=lfs -text
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+ calibrations/cam_cals/cam_cal_rosbag2_2026_01_08-15_24_20/calibration-report-cam.pdf filter=lfs diff=lfs merge=lfs -text
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+ calibrations/cam_cals/cam_cal_rosbag2_2026_01_14-08_47_13/calibration-report-cam.pdf filter=lfs diff=lfs merge=lfs -text
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+ calibrations/cam_cals/cam_cal_rosbag2_2026_01_20-16_31_50/calibration-report-cam.pdf filter=lfs diff=lfs merge=lfs -text
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+ calibrations/cam_cals/cam_cal_rosbag2_2026_02_01-08_50_41/calibration-report-cam.pdf filter=lfs diff=lfs merge=lfs -text
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+ calibrations/imu_cals/imu_cal_rosbag2_2026_01_04-16_18_59/calibration-report-imucam.pdf filter=lfs diff=lfs merge=lfs -text
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+ calibrations/imu_cals/imu_cal_rosbag2_2026_01_08-15_27_23/calibration-report-imucam.pdf filter=lfs diff=lfs merge=lfs -text
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+ calibrations/imu_cals/imu_cal_rosbag2_2026_01_14-08_55_16/calibration-report-imucam.pdf filter=lfs diff=lfs merge=lfs -text
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+ calibrations/imu_cals/imu_cal_rosbag2_2026_01_20-16_37_28/calibration-report-imucam.pdf filter=lfs diff=lfs merge=lfs -text
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+ calibrations/imu_cals/imu_cal_rosbag2_2026_02_01-08_57_08/calibration-report-imucam.pdf filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ pretty_name: OctoSense
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+ license: mit
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+ task_categories:
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+ - depth-estimation
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+ - image-segmentation
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+ - robotics
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+ tags:
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+ - autonomous-driving
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+ - multimodal
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+ - lidar
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+ - event-camera
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+ - stereo
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+ - optical-flow
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+ - ego-motion
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+ - sensor-fusion
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+ size_categories:
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+ - n>1T
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+ configs:
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+ - config_name: default
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+ data_files: metadata.parquet
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+ ---
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+ # Pre-Release State
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+
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+ # OctoSense
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+
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+ <p align="center">
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+ <img src="assets/hero.gif" alt="OctoSense — synchronized multi-sensor driving across day, night, and degraded conditions" width="100%"/>
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+ </p>
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+
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+ A time-synchronized, calibrated multi-sensor driving dataset: **371 sequences · 59.0 h · 2,474 km · 8.43 TB**,
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+ spanning urban, suburban, and rural driving (highway / residential / city) on **Long Island** and in
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+ **Philadelphia**, across sunrise, daytime, sunset, and nighttime. Sequences carry synchronized stereo RGB, stereo event cameras, infrared, Ouster LiDAR, two IMUs, GPS and CAN data, plus ground truth (depth, ego-motion optical flow, semantic segmentation) and a fused GPS + LiDAR-inertial odometry trajectory.
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+
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+ > Canonical key for every sequence is its recording datetime, `rosbag2_YYYY_MM_DD-HH_MM_SS`.
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+ > Per-sequence attributes live in **`metadata.parquet`** (the viewer's table: a sensor-montage thumbnail, a GPS-track map, and all metadata columns per sequence).
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+
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+ ## Sensors
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+
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+ | Modality | Sensor | Info | Rate |
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+ |---|---|---|---|
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+ | **RGB** (stereo) | 2× FLIR Blackfly S (Sony IMX421) | 1920×1456 | 100 Hz |
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+ | **Event** (stereo) | 2× SilkyEV VGA (Prophesee) | 640×480 | ≈7 MEv/s avg |
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+ | **Thermal** | FLIR A35 | 320×256 | 50 Hz |
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+ | **LiDAR** | Ouster OS1-64 | 64 beams × 2048 | 10 Hz |
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+ | **IMU** | VectorNav VN-100T | Acc/Gyro/Mag/Baro/Temp | 400 Hz |
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+ | **IMU** (in LiDAR) | IAM-20680HT | Acc/Gyro | 100 Hz |
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+ | **GNSS** | u-blox ZED-F9P | RTK (NTRIP) | 5 Hz |
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+ | **Vehicle** | 2021 Mazda CX-5 | CAN Signals | 50–100 Hz |
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+
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+ ![Per-sensor coordinate axes on the OctoSense rig (red = X, green = Y, blue = Z)](assets/cal_axis.png)
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+
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+ ## Coordinate frames & calibration
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+
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+ All extrinsics are stored per-sequence under `/calib` in the bag h5, named `A_T_B` — a 4×4 matrix that
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+ maps a point from frame **B** into frame **A** (e.g. `imgl_T_ouster` takes LiDAR points into the
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+ left-RGB frame). Frame abbreviations:
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+
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+ | abbrev | frame | | abbrev | frame |
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+ |---|---|---|---|---|
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+ | `imgl` / `imgr` | RGB left / right | | `ir` | Infrared (FLIR A35) |
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+ | `evl` / `evr` | Event left / right | | `ouster` | LiDAR |
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+ | `imu` | VectorNav IMU | | | |
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+
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+ Provided extrinsics: `imgl_T_imgr`, `imgl_T_imu`, `imgl_T_ir`, `imgr_T_imu`, `ir_T_imgl`, the event-pair
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+ set (`evl_T_evr`, `evl_T_imgl`, …), `ouster/imgl_T_ouster` / `ouster/ir_T_ouster`, and
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+ `calib/lidar_T_lidarimu` (LiDAR IMU → LiDAR/sensor frame, from the Ouster factory
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+ `imu_to_sensor_transform`). Each drive records which calibration it used via `rgb_cal_id` /
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+ `imu_cal_id` / `lidar_cal_id` (in the metadata). RKO-LIO odometry (`ouster/odom/*`) tracks the LiDAR pose in the **Map frame**;
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+ the fused `/ref` trajectory tracks the same LiDAR pose in the **UTM-relative world frame** (see Ground truth). The **depth**
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+ GT is rendered in the **rectified left-RGB camera image** (OpenCV `stereoRectify`, R1 applied). Use the
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+ `/calib` extrinsics (e.g. `imgl_T_ouster`) to move between frames.
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+
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+ > **Per-session calibration** is released alongside the data, in **Kalibr** format:
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+ > - **Cameras** (`calibration-camchain.yaml`) — a **4-camera Kalibr chain**: `cam0` = RGB right
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+ > (`/flir_cam_right`), `cam1` = RGB left (`/flir_cam_left`), `cam2` = event right (`/event_camera_right`,
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+ > 640×480), `cam3` = event left (`/event_camera_left`, 640×480). Each entry has pinhole
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+ > `intrinsics [fx,fy,cx,cy]` + radtan `distortion_coeffs`; each cam after `cam0` carries `T_cn_cnm1`
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+ > (transform from the previous cam in the chain).
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+ > - **Camera↔IMU** (`calibration-camchain-imucam.yaml`, adds `T_img[l/r]_imu`) + the **IMU** noise model (`calibration-imu.yaml`).
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+ > - **LiDAR↔camera** (`lidar_calibration_results.yaml`) and **IR** intrinsics + extrinsics (`ir_calib_result.json`).
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+ >
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+ > The processed h5 renames `cam0`/`cam1` to the `left`/`right` RGB streams above (and `cam2`/`cam3` are the right/left event cameras).
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+
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+ ![Ground-truth modalities: depth · semantic segmentation · odometry · optical flow](assets/gt.png)
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+
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+ ## Ground truth
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+
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+ - **Odometry / ego-motion** — **RKO-LIO** LiDAR-inertial odometry: SE(3) LiDAR pose in the **Map frame**
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+ + linear/angular velocity.
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+ - **Depth** — sparse metric depth in the rectified left-RGB image (`depth_cm`, uint16 cm,
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+ `0 = invalid`). Built by accumulating **61 LiDAR scans (≈6 s)**, removing dynamic objects (**YOLO26-medium**
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+ on the nearest RGB frame), keeping the **minimum** depth per pixel, and then projecting into the camera image.
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+ - **Optical flow** — **derived, not stored** — the ego-motion-induced flow (accumulated points projected into a
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+ future RGB frame). Regenerate from `depth + poses` with `derive_flow.py`.
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+ - **Semantic segmentation** — 19-class Cityscapes pseudo-labels ([**EoMT**](https://github.com/tue-mps/eomt)), on the **303 daytime sequences**
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+ (`has_seg = true`); night/degraded have no labels.
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+ - **Fused reference trajectory** — RKO-LIO odometry + GPS fused in a pose graph, under **`/ref`**
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+ (`T_world_lidar` (N,4,4) + `t`) in a **UTM-relative world frame** anchored at the first GPS fix.
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+ Geo-referencing + fusion quality live as `/ref` group attrs (`epsg`/`utm_zone`, `origin_{easting,northing,alt}_m`, GPS residual
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+ RMS/percentiles, pose-graph error). `ref/t` is **non-uniformly sampled** — poses are emitted at the union of the LiDAR-odom (≈10 Hz) and
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+ GPS (≈5 Hz) timestamps (≈13 Hz combined), so inter-pose intervals vary (≈0.02–0.1 s).
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+
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+ ## Per-sequence files (`<session>/<bag_id>/`)
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+
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+ | file | contents |
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+ |---|---|
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+ | `data.h5` | timestamps, IMU, GPS, CAN, LiDAR (range [deskewed by RKO] / sig / nir / refl), RKO-LIO odom, `/ref` (fused odometry), `/calib` |
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+ | `events.h5` | raw async event streams (`ev/left`, `ev/right`) — **≈78% of a sequence's bytes** |
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+ | `img_{left,right,infrared}.mp4` | per-camera H.265 encoded video |
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+ | `rgb_left_rect_depth.h5` | sparse metric depth in the rectified left-RGB image (`depth_cm`); flow derived via `derive_flow.py` |
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+ | `rgb_left_rect_semantic.h5` | 19-class Cityscapes pseudo-label seg in the rectified left-RGB image — **day sequences only** |
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+ | `captions.h5` | per-window scene captions (Gemma-4-31B VLM) + 4096-d Qwen3-Embedding-8B caption vectors + window metadata |
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+
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+ **Units/conventions:** depth in cm (`0`=invalid); event timestamps in µs; all other h5 time arrays in seconds.
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+
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+ > **Experimental — `car/radar` (Mazda forward radar).** Six object tracks decoded from the CAN data
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+ > with the [opendbc Mazda radar DBC](https://github.com/commaai/opendbc/blob/master/opendbc/dbc/mazda_radar.dbc) (CAN IDs 865–870 = `RADAR_TRACK_361..366`). Layout:
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+ > `car/radar/track_{1..6}/` with `t` (M,) float64 main-clock seconds, `dist` (M,) uint16, `ang` (M,) int16,
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+ > `vrel` (M,) int16. Values are stored **raw** (no scale/offset): `dist`=`DIST_OBJ` (sentinel `4095` = no track),
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+ > `ang`=`ANG_OBJ` (signed 12-bit), `vrel`=`RELV_OBJ` (signed 11-bit).
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+
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+ ## Data format
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+
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+ All `*/t` arrays are **seconds on a common PPS-synced main clock** (Kalman-smoothed); event times are
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+ **µs** on that same clock (`t/1e6` → s). Align streams **by timestamp** — RGB runs
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+ ≈100 Hz vs LiDAR ≈10 Hz. **Camera frames are raw (un-rectified)**: rectify with the per-camera `intrinsics` +
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+ radtan `dist_coeffs` (stored in the h5 and the camchain); the depth GT is already in the rectified left-RGB frame.
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+
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+ **Main bag h5 (`data.h5`)**:
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+
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+ | key | shape · dtype | meaning |
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+ |---|---|---|
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+ | `ouster/range_pcl` | (N,131072,3) int32 | LiDAR **XYZ in mm**, 64×2048 destaggered (`[0,0,0]` = no return) |
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+ | `ouster/{sig,nir}_pcl` · `refl_pcl` | (N,131072) uint16 · uint8 | per-point signal / near-IR / reflectivity |
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+ | `ouster/odom/map_T_lidart` | (N,4,4) f64 | RKO-LIO LiDAR pose in the **map frame** (translation **m**) (+ `lin_vel` m/s, `ang_vel` rad/s, `t` s); `hf_odom/*` = high-rate |
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+ | `ouster/{accel,ang_vel}` | (M,3) f32 | in-LiDAR IMU (IAM-20680HT); extrinsic to sensor frame = `calib/lidar_T_lidarimu` |
138
+ | `img/{left,right}/`, `infrared/` | — | `t` (≈100/100/50 Hz) · `intrinsics` (3,3) · `dist_coeffs` (4,) · `resolution` (2,) |
139
+ | `vectornav/` | (K,·) | `accel` (m/s²), `ang_vel` (rad/s) — each + `_raw` (uncompensated); `magnetic` (Gauss), `pressure` (Pa), `temperature` (°C), `t` (s); 400 Hz |
140
+ | `gps/data` | (G,7) f64 | `[lat°, lon°, alt_m, cov_xx, cov_yy, cov_zz, fix]`. `fix` = ROS `NavSatStatus.status`: **−1** no-fix · **0** standard fix · **1** SBAS · **2** GBAS/RTK. |
141
+ | `gps/velocity_enu` · `gps/heading` | (G,3) · (G,2) f64 | ENU velocity m/s · `[heading_rad, heading_acc]` |
142
+ | `car/` | (·,2+) f32 | CAN (col 0 = time, s): `speed` (mph), `wheels` (4× wheel speed, mph), `steer` (deg), `steer_rate` (deg/s), `vcc` = `[acc_x, acc_y]` (≈m/s²), `brake_on` (0/1), `pedal` & `brake_press` (raw counts) + `radar/` (above). **Experimental — decoded via a community Mazda DBC; units approximate/unverified.** |
143
+ | `ref/T_world_lidar` | (R,4,4) f64 | **fused** RKO-LIO+GPS pose, **UTM-relative world frame** (+ `ref/t`; geo-ref + quality in `/ref` attrs: `epsg`, `origin_{easting,northing}_m`, GPS residuals) |
144
+ | `calib/` | — | extrinsics `A_T_B` |
145
+
146
+ **Events (`events.h5`)** — flat arrays over all events, per side:
147
+
148
+ | key | dtype | meaning |
149
+ |---|---|---|
150
+ | `ev/{left,right}/t` | uint64 | event time **µs** (main clock) |
151
+ | `ev/{left,right}/{x,y}` | uint16 | pixel col / row, **raw 640×480** (un-rectified) |
152
+ | `ev/{left,right}/p` | uint8 | polarity (0/1) |
153
+ | `ev/{left,right}/ms_to_idx` | uint64 | millisecond → event index |
154
+
155
+ **Depth GT (`rgb_left_rect_depth.h5`)** — per-LiDAR-scan sparse depth in the **rectified left-RGB** image.
156
+ `N` = `n_lidar_frames`; frame `k` ↔ LiDAR scan `k`. Carries its own `timestamps`
157
+ (identical to `data.h5` `ouster/t`) and index arrays:
158
+
159
+ | key | shape · dtype | meaning |
160
+ |---|---|---|
161
+ | `depth_cm` | (N,1456,1920) uint16 | metric depth in **cm** (`m = depth_cm/100`), `0 = invalid` |
162
+ | `timestamps` | (N,) f64 | main-clock seconds (= `data.h5` `ouster/t`) |
163
+ | `lidar_indices` | (N,) int64 | LiDAR-scan index (`0..N−1`) |
164
+ | `left_img_indices` | (N,) int64 | matching frame in `img_left.mp4`|
165
+ | `poses` | (N,4,4) f32 | per-scan LiDAR pose used to accumulate the depth (input to `derive_flow.py`) |
166
+ | `K_rect` · `R3` | (3,3) f64 | rectified left-RGB intrinsics · rectification rotation (`R1`) |
167
+ | `imgl_T_ouster` | (4,4) f64 | LiDAR → **raw (unrectified)** left-RGB cam |
168
+ | `raw_res` | (2,) int32 | source image resolution |
169
+
170
+ Root attrs: `depth_scale_cm`, `flow_{gap,scale,invalid}`, `flow_stored` (=`False` — flow is **derived, not stored**), `n_scans`.
171
+
172
+ **Semantic GT (`rgb_left_rect_semantic.h5`, day sequences only)** — per-LiDAR-scan 19-class seg in the
173
+ **rectified left-RGB** image, aligned 1:1 with `rgb_left_rect_depth.h5`:
174
+
175
+ | key | shape · dtype | meaning |
176
+ |---|---|---|
177
+ | `semantic` | (N,1456,1920) uint8 | Cityscapes class id `0..18` (see `classes` attr); `255` = ignore |
178
+ | `lidar_indices` | (N,) int64 | LiDAR-scan index for each frame (`0..N−1`) |
179
+ | `left_img_indices` | (N,) int64 | matching frame in `img_left.mp4` (RGB ≈100 Hz vs LiDAR ≈10 Hz) |
180
+ | `timestamps` | (N,) f64 | main-clock seconds |
181
+
182
+ Root attrs: `classes` (19 names), `num_classes=19`, `model=EoMT-Cityscapes-DINOv2-L-1024`,
183
+ `coordinate_frame=rectified_left`, `preprocessing=rectify+CLAHE`, `resolution=1920x1456`.
184
+ So `semantic[k]` ↔ `depth_cm[k]` ↔ `img_left.mp4[left_img_indices[k]]` ↔ `ouster/t[k]`.
185
+
186
+ **Captions & semantic search (`captions.h5`)** — each sequence is split into **≈5 s windows** (`W` per
187
+ sequence ≈ duration / 5 s); every window gets a natural-language scene caption (a **Gemma-4-31B** VLM) and a
188
+ 4096-d text embedding (**Qwen3-Embedding-8B**) for free-text retrieval:
189
+
190
+ | key | shape · dtype | meaning |
191
+ |---|---|---|
192
+ | `captions` | (W,) str | one scene caption per window |
193
+ | `embeddings` | (W,4096) f32 | Qwen3-Embedding-8B vector for each caption |
194
+ | `metadata` | (W,) struct | `window_id`, `frame_idx` (→ `img_left.mp4` frame), `timestamp` (main-clock s), `speed_mps`, `turn_deg`, `dist_m`, `is_night` |
195
+
196
+ Root attrs: `caption_model`, `embed_model`, `embed_dim` (4096), `num_windows`, `video_id`.
197
+
198
+ ## Splits
199
+
200
+ `train` = 293 · `test` = 78 (bag-level; preserved across dataset versions). The dataset contains daytime, nighttime, and degraded sequences.
201
+
202
+ ## `metadata.parquet` fields (27)
203
+
204
+ `bag_id, session, start_time, split, is_daytime, degraded, has_seg,
205
+ duration_s, n_lidar_frames, n_rgb_frames, n_imu_samples, n_events_left, n_events_right,
206
+ n_gps_fixes, n_gps_valid, gps_quality, gps_lat_min/max, gps_lon_min/max,
207
+ mean_speed_mph, idle_fraction, distance_m, rgb_cal_id, imu_cal_id, lidar_cal_id, sensor_dropout`
208
+
209
+ - `gps_quality` ∈ {`RTK_fixed_cm`, `float_dm`, `single_m`, `no_fix`, `absent`} — 67 / 62 / 239 / 2 / 1.
210
+ - `sensor_dropout` — `null`, or `sensor:seconds[;sensor:seconds]`
211
+
212
+ ## Loading
213
+
214
+ ```python
215
+ import h5py, hdf5plugin, numpy as np, pyarrow.parquet as pq
216
+ meta = pq.read_table("metadata.parquet").to_pydict() # per-sequence table
217
+ with h5py.File("<session>/<bag_id>/data.h5") as f:
218
+ lidar = f["ouster/range_pcl"][:]; odom = f["ouster/odom/map_T_lidart"][:]
219
+
220
+ # RGB / IR frames — native-res H.265 mp4 decoded with torchcodec.
221
+ from torchcodec.decoders import VideoDecoder
222
+ dec = VideoDecoder("<session>/<bag_id>/img_left.mp4") # 1920x1456; len(dec) == n_rgb_frames
223
+ rgb_k = dec[k] # (3, H, W) uint8 tensor at frame k
224
+ # RGB runs ≈100 Hz vs LiDAR ≈10 Hz — align by TIMESTAMP, not shared index:
225
+ with h5py.File("<session>/<bag_id>/data.h5") as f:
226
+ img_t = f["img/left/t"][:]; lid_t = f["ouster/t"][:] # seconds, same main clock
227
+ k = int(np.argmin(np.abs(img_t - lid_t[j]))) # RGB frame nearest LiDAR frame j
228
+ # img_right.mp4 / img_infrared.mp4 decode the same way (IR is grayscale-as-video, ≈50 Hz).
229
+
230
+ # Depth + semantic GT — one frame per LiDAR scan, in the rectified left-RGB image:
231
+ with h5py.File("<session>/<bag_id>/rgb_left_rect_depth.h5") as f:
232
+ depth_m = f["depth_cm"][j] / 100.0 # (1456,1920); 0 = invalid (j = LiDAR scan)
233
+ with h5py.File("<session>/<bag_id>/rgb_left_rect_semantic.h5") as f: # day sequences only
234
+ seg = f["semantic"][j] # (1456,1920) uint8, class 0..18
235
+ rgb_idx = f["left_img_indices"][j] # matching img_left.mp4 frame for scan j
236
+
237
+ # flow is derived, not stored:
238
+ from derive_flow import derive_flow_from_h5
239
+ flow_uv = derive_flow_from_h5("<session>/<bag_id>/rgb_left_rect_depth.h5", frame_idx, poses=odom)
240
+ ```
241
+
242
+ ## Statistics
243
+
244
+ 371 sequences · 59.0 h · 2,474 km · 8.43 TB · 303 day / 68 night · 4 degraded · segmentation on 303 · GPS: 67 RTK / 62 float / 239 single / 2 no-fix / 1 absent.
245
+
246
+ ## Notes/Known limitations
247
+
248
+ - **Optical flow is derived solely from ego-motion** — rigid camera-motion reprojection of LiDAR depth; it does **not**
249
+ capture independent motion of dynamic objects.
250
+ - **Seg is day-only** (303 sequences); night/degraded have no segmentation labels.
251
+ - **IMU compensated vs raw.** VectorNav factory-calibrates each unit (bias/scale/axis-misalignment + temperature applied). In this dataset `accel` ≡ `accel_raw` (bit-identical); `ang_vel` additionally has the on-board EKF's real-time gyro-bias estimate removed.
252
+ - **5 sequences have a >10 s sensor dropout** (see `sensor_dropout`): 3 with the event camera off early
253
+ (20–43 s), 1 GPS+CAN, 1 CAN. **3 sequences have no usable GPS** (`no_fix`/`absent`).
254
+ - **IR & GPS are PPS-synced indirectly, through the IMU PPS-system clock calibration.**
255
+ - **LiDAR clock is PPS-stepped, not Kalman-aligned.** Unlike the other streams, the Ouster clock is not
256
+ actively Kalman-smoothed onto the main clock; its internal clock only advances its whole-second
257
+ counter when the PPS edge arrives.
258
+ - **IMU is noisy from vehicle vibration.** Both IMUs pick up road and engine vibration; the sensors are
259
+ soft-mounted to dampen it, but residual vibration noise remains in the accelerometer / gyro signals.
260
+ - **IR frame rate occasionally dips below 50 Hz.** The infrared camera is not run as a composable node, so
261
+ under load it sometimes falls below its nominal 50 Hz capture rate (gaps visible in `infrared/t`).
262
+ - **RKO-LIO cold-start transient.** The estimator's initial phase can occasionally be jerky — at the start of a
263
+ sequence the LIO briefly reports ≈zero motion, then "catches up" with a jump once it converges →
264
+ jerky roll/pitch (occasionally z) over the first few meters of motion (upstream RKO-LIO issue #139).
265
+ - **LiDAR deskew is a constant-motion approximation.** Each ~100 ms sweep is motion-compensated using the
266
+ **average** body acceleration `a` and **average** angular velocity `ω` over the sweep — a point at offset
267
+ `dt` from the scan reference time is warped by `exp([ v·dt + ½·a·dt², ω·dt ])` (constant-acceleration
268
+ translation + constant-angular-velocity rotation). Because `a`/`ω` are held constant across the sweep,
269
+ rapid intra-sweep motion (high jerk, sharp turns, potholes) leaves some residual skew; `a` is
270
+ Kalman-filtered + jerk-bounded to limit this but cannot fully remove it.
271
+
272
+ ## Citation
273
+ ```bibtex
274
+ @misc{bisulco2026octosense,
275
+ title = {{OctoSense}: Self-Supervised Learning for Multimodal Robot Perception},
276
+ author = {Bisulco, Anthony and Wang, Jeremy and Daniilidis, Kostas and Balestriero, Randall and Chaudhari, Pratik},
277
+ year = {2026},
278
+ howpublished = {Preprint},
279
+ }
280
+ ```
281
+
282
+ ## License
283
+
284
+ Released under the **MIT License** — free to use, modify, and redistribute with attribution; provided "as is" without warranty. If you use OctoSense, please cite the paper (see Citation above).
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1
+ cam0:
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+ cam_overlaps: [1, 2, 3]
3
+ camera_model: pinhole
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+ distortion_coeffs: [-0.15385396380802188, 0.0749641388870442, 0.001000267559278542, 0.002804201584318143]
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+ distortion_model: radtan
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+ intrinsics: [1417.501796249508, 1413.430442569655, 988.1439401050002, 733.8064858691746]
7
+ resolution: [1920, 1456]
8
+ rostopic: /flir_cam_right
9
+ cam1:
10
+ T_cn_cnm1:
11
+ - [0.9995593640170585, -0.007529703240476874, 0.02871204233227203, 0.3838725843195073]
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+ - [0.005717000260326077, 0.998015962148046, 0.0627013174159818, 0.0009849047658489283]
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+ - [-0.029127198866407675, -0.06250954220586345, 0.9976192477189764, -0.0019515762440131853]
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+ - [0.0, 0.0, 0.0, 1.0]
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+ cam_overlaps: [0, 2, 3]
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+ camera_model: pinhole
17
+ distortion_coeffs: [-0.15589065459647117, 0.06084534489493509, -0.00234409054407442, -0.0026530574846732736]
18
+ distortion_model: radtan
19
+ intrinsics: [1428.3251206496743, 1423.8837606531902, 902.691023138746, 632.4383854230887]
20
+ resolution: [1920, 1456]
21
+ rostopic: /flir_cam_left
22
+ cam2:
23
+ T_cn_cnm1:
24
+ - [0.9999095568056536, 0.007131281197360464, -0.011402764454651456, -0.2765923364369594]
25
+ - [-0.007491974492247794, 0.9994627085780684, -0.031908689725550894, 0.001973280420442845]
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+ - [0.01116908800805142, 0.031991233022160025, 0.9994257413549008, 0.0007015331518512155]
27
+ - [0.0, 0.0, 0.0, 1.0]
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+ cam_overlaps: [0, 1, 3]
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+ camera_model: pinhole
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+ distortion_coeffs: [-0.16106104491840462, 0.08215485720807618, -0.0009966249214452445, -0.002603151199575899]
31
+ distortion_model: radtan
32
+ intrinsics: [427.79848694363847, 427.3455387218023, 311.33829577023766, 228.50968889775194]
33
+ resolution: [640, 480]
34
+ rostopic: /event_camera_right
35
+ cam3:
36
+ T_cn_cnm1:
37
+ - [0.9999988997457284, 0.001238941642642881, 0.0008158007899400578, 0.37992465909227136]
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+ - [-0.0012351223343334272, 0.999988353044184, -0.004665645591967993, -0.0007861317242734042]
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+ - [-0.000821571750958009, 0.004664632844795503, 0.9999887830472257, -0.012386021719521651]
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+ - [0.0, 0.0, 0.0, 1.0]
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+ cam_overlaps: [0, 1, 2]
42
+ camera_model: pinhole
43
+ distortion_coeffs: [-0.1568936987135577, 0.07657681907205878, 0.0008004354838615022, -0.0012859496766683847]
44
+ distortion_model: radtan
45
+ intrinsics: [427.5042540144832, 426.1324526071299, 313.2063248051701, 223.6201600911985]
46
+ resolution: [640, 480]
47
+ rostopic: /event_camera_left
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1
+ Calibration results
2
+ ====================
3
+ Camera-system parameters:
4
+ cam0 (/flir_cam_right):
5
+ type: <class 'aslam_cv.libaslam_cv_python.DistortedPinholeCameraGeometry'>
6
+ distortion: [-0.15385396 0.07496414 0.00100027 0.0028042 ] +- [0.00073926 0.00114316 0.00008007 0.00008395]
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+ projection: [1417.50179625 1413.43044257 988.14394011 733.80648587] +- [0.14282976 0.15942435 0.03996132 0.24838398]
8
+ reprojection error: [-0.002576, -0.011486] +- [1.106160, 1.284410]
9
+
10
+ cam1 (/flir_cam_left):
11
+ type: <class 'aslam_cv.libaslam_cv_python.DistortedPinholeCameraGeometry'>
12
+ distortion: [-0.15589065 0.06084534 -0.00234409 -0.00265306] +- [0.00066284 0.00104279 0.00008345 0.00006756]
13
+ projection: [1428.32512065 1423.88376065 902.69102314 632.43838542] +- [0.15164593 0.16250659 0.03110859 0.23649301]
14
+ reprojection error: [0.002399, 0.009587] +- [1.099249, 1.085763]
15
+
16
+ cam2 (/event_camera_right):
17
+ type: <class 'aslam_cv.libaslam_cv_python.DistortedPinholeCameraGeometry'>
18
+ distortion: [-0.16106104 0.08215486 -0.00099662 -0.00260315] +- [0.00192452 0.00300279 0.00021092 0.00015584]
19
+ projection: [427.79848694 427.34553872 311.33829577 228.5096889 ] +- [0.18781801 0.20852822 0.13149429 0.34753077]
20
+ reprojection error: [0.001266, 0.001172] +- [0.411296, 0.386043]
21
+
22
+ cam3 (/event_camera_left):
23
+ type: <class 'aslam_cv.libaslam_cv_python.DistortedPinholeCameraGeometry'>
24
+ distortion: [-0.1568937 0.07657682 0.00080044 -0.00128595] +- [0.00208104 0.00347396 0.00023031 0.00015028]
25
+ projection: [427.50425401 426.13245261 313.20632481 223.62016009] +- [0.1683201 0.17927078 0.00461333 0.05233623]
26
+ reprojection error: [0.000858, -0.002563] +- [0.489927, 0.427000]
27
+
28
+ baseline T_1_0:
29
+ q: [ 0.03132153 -0.0144685 -0.00331367 0.99939914] +- [0.00034278 0.0000651 0.00004473]
30
+ t: [ 0.38387258 0.0009849 -0.00195158] +- [0.00006778 0.0000523 0.0002185 ]
31
+
32
+ baseline T_2_1:
33
+ q: [-0.01597738 0.00564381 0.00365636 0.99984974] +- [0.00089633 0.00035066 0.00011301]
34
+ t: [-0.27659234 0.00197328 0.00070153] +- [0.00015098 0.00015531 0.00057625]
35
+
36
+ baseline T_3_2:
37
+ q: [-0.00233258 -0.00040934 0.00061852 0.999997 ] +- [0.00081957 0.00036337 0.00015682]
38
+ t: [ 0.37992466 -0.00078613 -0.01238602] +- [0.00020842 0.00020971 0.0006807 ]
39
+
40
+
41
+
42
+ Target configuration
43
+ ====================
44
+
45
+ Type: aprilgrid
46
+ Tags:
47
+ Rows: 6
48
+ Cols: 6
49
+ Size: 0.1121 [m]
50
+ Spacing 0.028025 [m]
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+ Calibration results
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+ ====================
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+ Camera-system parameters:
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+ type: <class 'aslam_cv.libaslam_cv_python.DistortedPinholeCameraGeometry'>
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+ ====================
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+ Cols: 6
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+ Size: 0.1121 [m]
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+ ====================
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+ Camera-system parameters:
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+ ====================
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+ ====================
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