Datasets:

anthonytec2
/

OctoSense

@@ -37,6 +37,9 @@ A time-synchronized, calibrated multi-sensor driving dataset: **371 sequences ·
 spanning urban, suburban, and rural driving (highway / residential / city) on **Long Island** and in
 **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.
 > Canonical key for every sequence is its recording datetime, `rosbag2_YYYY_MM_DD-HH_MM_SS`.
 > 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).
@@ -211,7 +214,7 @@ duration_s, n_lidar_frames, n_rgb_frames, n_imu_samples, n_events_left, n_events
 n_gps_fixes, n_gps_valid, gps_quality, gps_lat_min/max, gps_lon_min/max,
 mean_speed_mph, idle_fraction, distance_m, rgb_cal_id, imu_cal_id, lidar_cal_id, sensor_dropout`
-- `gps_quality` ∈ {`RTK_fixed_cm`, `float_dm`, `single_m`, `no_fix`, `absent`} — 67 / 62 / 239 / 2 / 1.
 - `sensor_dropout` — `null`, or `sensor:seconds[;sensor:seconds]`
 ## Loading
@@ -268,18 +271,22 @@ flow_uv = derive_flow_from_h5("<session>/<bag_id>/rgb_left_rect_depth.h5", frame
 - **RKO-LIO cold-start transient.** The estimator's initial phase can occasionally be jerky — at the start of a
   sequence the LIO briefly reports ≈zero motion, then "catches up" with a jump once it converges →
   jerky roll/pitch (occasionally z) over the first few meters of motion (upstream RKO-LIO issue #139).
-- **LiDAR deskew is a constant-motion approximation.** Each ~100 ms sweep is motion-compensated using the
-  **average** body acceleration `a` and **average** angular velocity `ω` over the sweep — a point at offset
   `dt` from the scan reference time is warped by `exp([ v·dt + ½·a·dt²,  ω·dt ])` (constant-acceleration
   translation + constant-angular-velocity rotation). Because `a`/`ω` are held constant across the sweep,
   rapid intra-sweep motion (high jerk, sharp turns, potholes) leaves some residual skew; `a` is
   Kalman-filtered + jerk-bounded to limit this but cannot fully remove it.
 - **LiDAR noise can leak into the depth GT.** The depth ground truth is projected directly from the raw
-  Ouster returns, so sensor noise (stray or spurious returns from rain, snow, fog, dust, retroreflectors,
   or specular/multi-path reflections) can survive into our depth ground truth as a small number of
   erroneous points.
 - **Platform Bounce.** On a few sequences the SeaSuckers loosened, resulting in vertical motion (bounce) of the platform.
 ## Citation
 ```bibtex
 @misc{bisulco2026octosense,

 spanning urban, suburban, and rural driving (highway / residential / city) on **Long Island** and in
 **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.
+Upon release, we will provide code for using the dataset and deriving flow.
 > Canonical key for every sequence is its recording datetime, `rosbag2_YYYY_MM_DD-HH_MM_SS`.
 > 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).
 n_gps_fixes, n_gps_valid, gps_quality, gps_lat_min/max, gps_lon_min/max,
 mean_speed_mph, idle_fraction, distance_m, rgb_cal_id, imu_cal_id, lidar_cal_id, sensor_dropout`
+- `gps_quality` ∈ {`RTK_fixed_cm`, `float_dm`, `single_m`, `no_fix`, `absent`} — 67 / 62 / 239 / 2 / 1. Tiers are the **receiver-reported** horizontal σ (`max(√cov_xx, √cov_yy)` from `gps/data`, over 1.06M fixes): RTK-fixed **≈1–3 cm**, float **≈0.1–0.5 m**, single **≈0.5–3 m**. The released `fused_traj` refines these and reports per-sequence residuals.
 - `sensor_dropout` — `null`, or `sensor:seconds[;sensor:seconds]`
 ## Loading
 - **RKO-LIO cold-start transient.** The estimator's initial phase can occasionally be jerky — at the start of a
   sequence the LIO briefly reports ≈zero motion, then "catches up" with a jump once it converges →
   jerky roll/pitch (occasionally z) over the first few meters of motion (upstream RKO-LIO issue #139).
+- **LiDAR deskew is a constant-motion approximation.** Each ~100 ms sweep is
+  [motion-compensated by RKO-LIO](https://github.com/PRBonn/rko_lio/blob/a67dd406caaa8229d3ff858f2f38519eb8097831/rko_lio/core/lio.cpp#L437)
+  using the **average** body acceleration `a` and **average** angular velocity `ω` over the sweep — a point at offset
   `dt` from the scan reference time is warped by `exp([ v·dt + ½·a·dt²,  ω·dt ])` (constant-acceleration
   translation + constant-angular-velocity rotation). Because `a`/`ω` are held constant across the sweep,
   rapid intra-sweep motion (high jerk, sharp turns, potholes) leaves some residual skew; `a` is
   Kalman-filtered + jerk-bounded to limit this but cannot fully remove it.
 - **LiDAR noise can leak into the depth GT.** The depth ground truth is projected directly from the raw
+  Ouster returns, so sensor noise (stray or spurious returns from rain, snow, fog, dust, retroreflectors
+  (see [LiDAR ghosts & blooming](https://www.teachkidsrobotics.com/blog/what-are-lidar-ghosts-and-blooming/)),
   or specular/multi-path reflections) can survive into our depth ground truth as a small number of
   erroneous points.
 - **Platform Bounce.** On a few sequences the SeaSuckers loosened, resulting in vertical motion (bounce) of the platform.
+We have taken great care to perform data quality checks on this data. That said, some issues at this scale may slip through, so should you find any examples of gross desynchronization, please report them and we can take a look.
 ## Citation
 ```bibtex
 @misc{bisulco2026octosense,