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README.md

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 license: bsd-3-clause
 ---
 
-This repository contains datasets released in relation with the Unified Autonomy Stack. Details can be found here: https://ntnu-arl.github.io/unified_autonomy_stack/
+# Unified Autonomy Stack Datasets
+
+This repository contains data released in relation with the Unified Autonomy Stack. Details can be found here
+
+- Paper: <https://arxiv.org/abs/2605.12735>
+- Code: <https://ntnu-arl.github.io/unified_autonomy_stack/>
+
+The data was collected using the following platforms in both manually piloted and autonomously operated modes:
+
+- **AR-1 (Hornbill):** A variant of the [RMF-Owl](https://ieeexplore.ieee.org/document/9836115) collision-tolerant aerial robot.
+- **AR-2 (Magpie):** A collision-tolerant aerial robot designed to carry the UniPilot module.
+- **GR-1 (Anymal):** An ANYmal D quadruped robot from ANYbotics carrying the UniPilot module.
+- **UniPilot:** A compact hardware-software autonomy payload that can be integrated across diverse robot embodiments to enable autonomous operation in GPS-denied environments. In addition to being carried by AR-2 and GR-1, a handheld (helmet-mounted) variant was used for the `campus_fog` sequence.
+
+![Platforms](assets/platforms.png)
+
+The following table summarizes the platforms used for each dataset:
+
+|  Name | Location (Norway) | Platform(s) | Notes |
+|--------------|----------|-------------|-------------|
+| basement/nmpc | NTNU Elektro Building basement | AR-2 (Magpie) | Autonomous Full Stack (Navigation mode: NMPC) |
+| basement/rl | NTNU Elektro Building basement | AR-2 (Magpie) | Autonomous Full Stack (Navigation mode: RL) |
+| elektro_hall | NTNU Elektro Building | GR-1 (UniPilot-Anymal) | Autonomous Full Stack |
+| løkken_mine | Løkken mine | AR-2 (Magpie) | Autonomous Full Stack |
+| runehamar_tunnel/magpie | Runehamar tunnel | AR-2 (Magpie) | Autonomous Full Stack |
+| runehamar_tunnel/hornbill | Runehamar tunnel | AR-1 (Hornbill) | Manually Piloted, SLAM Evaluation |
+| frozen_lake | Jonsvatnet Lake | AR-1 (Hornbill) | Manually Piloted, SLAM Evaluation |
+| fyllingsdalen_tunnel | Fyllingsdalen tunnel | AR-1 (Hornbill) | Manually Piloted, SLAM Evaluation |
+| campus_fog | NTNU campus | UniPilot | Helmet-mounted walking, SLAM Evaluation |
+
+## Sensor Setup
+
+| Sensor | AR-1 (Hornbill) | UniPilot (AR-2 / GR-1 / handheld) |
+|:------:|:----------------|:----------------------------------|
+| LiDAR  | [Ouster OS0-128 Rev7](https://ouster.com/products/hardware/os0-lidar-sensor) | [RoboSense Airy](https://www.robosense.ai/en/rslidar/Airy); [Hesai JT-128](https://www.hesaitech.com/product/jt128/) on the handheld `campus_fog` |
+| Camera | [FLIR Blackfly S 0.4 MP Color](https://www.teledynevisionsolutions.com/en-150/products/blackfly-s-usb3/?model=BFS-U3-04S2C-C&vertical=machine%20vision&segment=iis) | 3× [MIPI Vision Components IMX296-C](https://www.mipi-modules.com/en/mipi-camera-modules-technical-data/) |
+| Radar  | [TI IWR6843AOP](https://www.ti.com/tool/IWR6843AOPEVM) | [D3 Embedded RS-6843AOPU FMCW](https://www.d3embedded.com/product/designcore-rs-6843aopu-mmwave-radar-sensor/); [uRAD Industrial](https://urad.es/en/product/urad-radar-industrial/) on the handheld `campus_fog` |
+| IMU    | [VectorNav VN-100](https://www.vectornav.com/products/detail/vn-100) | [VectorNav VN-100](https://www.vectornav.com/products/detail/vn-100) |
+| Compute | [Khadas VIM4](https://www.khadas.com/vim4) | [NVIDIA Jetson Orin NX](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/) |
+
+## Data Description
+
+Each sequence is released as a **ROS 1 bag** named `sensors_only.bag` containing the raw sensor data collected during the run. The ground truth is provided as a `.tum` file for the sequences where it is available.
+
+Due to resource constraints on the platforms, the LiDAR is recorded as **raw packets** instead of the deserialized point clouds. To obtain bags with the point clouds, please view the [lidar_packets_to_pointclouds.md](lidar_packets_to_pointclouds.md).
+
+### Topics
+
+The radar, IMU, and static transforms are common to all platforms; only the LiDAR and camera topics differ. Bags may additionally contain: flight-controller telemetry under `/mavros/*` (including GNSS as `sensor_msgs/NavSatFix` and `mavros_msgs/GPSRAW` where a fix was available), hardware time-synchronization triggers and status under `/sensor_sync_node/*`, further VN-100 streams (temperature, filtered IMU), and per-sensor host-receive timestamps under `/<sensor>/ros_time_now`.
+
+#### Common to all platforms
+
+| Sensor | Topic | Datatype | Rate |
+| --- | --- | --- | --- |
+| Radar | `/radar/cloud` | `sensor_msgs/PointCloud2` | 10 Hz |
+| IMU | `/vectornav_driver_node/imu/data` | `sensor_msgs/Imu` | 200 Hz |
+| Magnetometer | `/vectornav_driver_node/imu/mag` | `sensor_msgs/MagneticField` | 200 Hz |
+| Barometer | `/vectornav_driver_node/pressure` | `sensor_msgs/FluidPressure` | 200 Hz |
+| Extrinsics (static transforms) | `/tf_static` | `tf2_msgs/TFMessage` | — |
+
+#### LiDAR
+
+Recorded as raw packets corresponding to a 10 Hz point cloud (one LiDAR per platform):
+
+| Platform (LiDAR) | Packets | LiDAR IMU | Other |
+| --- | --- | --- | --- |
+| AR-1 (Ouster OS0-128) | `/ouster/lidar_packets` (`ouster_ros/PacketMsg`) | `/ouster/imu_packets` (`ouster_ros/PacketMsg`, 100 Hz) | `/ouster/metadata` (`std_msgs/String`) |
+| UniPilot AR-2 / GR-1 (RoboSense Airy) | `/rslidar_packets` (`rslidar_msg/RslidarPacket`) | `/rslidar_imu_data` (`sensor_msgs/Imu`, 200 Hz) | — |
+| UniPilot handheld (Hesai JT-128) | `/lidar_packets` (`hesai_ros_driver/UdpFrame`) | `/lidar_imu` (`sensor_msgs/Imu`) | `/lidar_packets_loss` (`hesai_ros_driver/LossPacket`) |
+
+#### Cameras
+
+`sensor_msgs/CompressedImage` at 20 Hz:
+
+| Platform | Topic(s) |
+| --- | --- |
+| AR-1 (Hornbill) | `/cam0/cam0/compressed` (intrinsics on `/cam0/camera_info`) |
+| UniPilot (AR-2 / GR-1 / handheld) | `/cam_front/image_raw/compressed`, `/cam_left/image_raw/compressed`, `/cam_right/image_raw/compressed` |
+
+> On `fyllingsdalen_tunnel`, the AR-1 camera and radar were recorded at 25 Hz (the radar chirp configuration was changed for high-speed flight).
+
+### Calibration
+
+#### Intrinsics
+
+Camera intrinsics are provided in the [calibration folder](calibration/), with the same intrinsics for all sequences recorded on a given platform.
+
+#### Extrinsics
+
+The transforms between sensors are provided below. The transform $T_{AB}$ transforms a point from frame $B$ to frame $A$ as $p_A = T_{AB} * p_B$, where the point is represented in homogeneous coordinates. All transforms are provided in the format: `x, y, z, qx, qy, qz, qw`.
+
+```
+AR-1 (Hornbill):
+  T_imu_lidar: [0.0166, 0.02158, 0.03375, 0, 0, 0, 1] 
+  T_imu_radar: [0.077, 0.016, -0.063, 0.963, -0.021, -0.265, 0.021]
+  T_imu_cam: [0.0725765278611583, 0.018936068067624674, -0.03560091123164558, 0.5543417213240229, -0.5433799916950063, 0.44199772312966146, 0.4495347076402992]
+
+AR-2 / GR-1 (UniPilot):
+  T_imu_lidar: [-0.06605, -0.01878, 0.034, 0.707, 0.00, -0.707, 0.00]
+  T_imu_radar: [0.07717380907196035, -0.0479741168664902, 0.006770362043579366, 0.01761050968654745, 0.25032180256367187, -0.017699746191127304, 0.9679631109162291]
+
+UniPilot handheld:
+  T_imu_lidar: [-0.11484, -0.01878, 0.035, 0.3799282, -0.5963678, -0.3799282, 0.5963678]
+  T_imu_radar: [0.08373, 0.0213, 0.02691, 0.6830127, 0.1830128, -0.1830128, 0.6830127]
+  T_imu_cam_front: [0.083743715, 0.000235985, 0.005836153, -0.608415182, 0.616574414, -0.353450887, 0.353184694]
+  T_imu_cam_left: [-0.021764534, 0.045545798, 0.023474280, -0.706455147, 0.000556152, -0.002366216, 0.707753642]
+  T_imu_cam_right: [-0.025516567, -0.089169163, 0.024118153, 0.008477527, 0.715010265, -0.699047196, -0.004633580]
+
+```
+
+## Ground Truth
+
+Where available, ground truth is provided as a [TUM-format](https://vision.in.tum.de/data/datasets/rgbd-dataset/file_formats) trajectory file (`timestamp tx ty tz qx qy qz qw`) alongside the bag.
+
+- **Tunnels (`fyllingsdalen_tunnel`, `runehamar_tunnel/hornbill`):** generated by fusing the tracking of a Leica GRZ101 mini-prism (mounted on AR-1) by a Leica MS60 MultiStation with the onboard IMU, in an offline Levenberg-Marquardt optimization.
+- **`campus_fog` and `frozen_lake`:** GNSS was available, so ground truth is created using a GNSS-augmented visual bundle adjustment optimization with Pix4DMatic.
+
+## Citation
+
+If you use this data in your research, please cite the following publication:
+
+```
+@misc{dharmadhikari2026unifiedautonomystackblueprint,
+      title={The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy}, 
+      author={Mihir Dharmadhikari and Nikhil Khedekar and Mihir Kulkarni and Morten Nissov and Martin Jacquet and Angelos Zacharia and Marvin Harms and Albert Gassol Puigjaner and Philipp Weiss and Kostas Alexis},
+      year={2026},
+      eprint={2605.12735},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2605.12735}, 
+}
+```

calibration/ar1_cam0.yaml

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+image_width: 720
+image_height: 540
+camera_name: cam0
+camera_matrix:
+  rows: 3
+  cols: 3
+  data:
+    [
+      590.8133766054999,
+      0.0,
+      368.7283968476789,
+      0.0,
+      590.9751604077278,
+      277.3759655823565,
+      0.0,
+      0.0,
+      1.0,
+    ]
+distortion_model: equidistant
+distortion_coefficients:
+  rows: 1
+  cols: 4
+  data:
+    [
+      -0.029369948274627956,
+      0.04993257519047628,
+      -0.13006659026031173,
+      0.10029538538076066,
+    ]

calibration/unipilot_handheld/cam_front.yaml

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+cam0:
+  cam_overlaps: []
+  camera_model: pinhole
+  distortion_coeffs: [-0.040671113739690706, 0.003126177356056777, -0.004046152244583798, 0.000943198002567342]
+  distortion_model: equidistant
+  intrinsics: [689.9272234960254, 689.9163888108998, 663.9428319007329, 499.3552239182778]
+  resolution: [1440, 1080]
+  rostopic: /cam_front/synchronized/image_raw/compressed

calibration/unipilot_handheld/cam_left.yaml

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+cam0:
+  cam_overlaps: []
+  camera_model: pinhole
+  distortion_coeffs: [-0.038448367749780395, -0.0025663619405085067, 0.0008604117251419172, -0.00048354193161735133]
+  distortion_model: equidistant
+  intrinsics: [694.1894966936176, 694.5667443411297, 714.2098100544231, 492.693257879215]
+  resolution: [1440, 1080]
+  rostopic: /cam_left/synchronized/image_raw/compressed

calibration/unipilot_handheld/cam_right.yaml

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+cam0:
+  cam_overlaps: []
+  camera_model: pinhole
+  distortion_coeffs: [-0.03845085417046973, 0.0010879258236332645, -0.002622019925992595, 0.0005462886865225762]
+  distortion_model: equidistant
+  intrinsics: [693.6198023798082, 693.5025025756963, 717.3719574047927, 492.6127655064589]
+  resolution: [1440, 1080]
+  rostopic: /cam_right/synchronized/image_raw

lidar_packets_to_pointclouds.md

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+# LiDAR Packets to Point Clouds
+
+The LiDAR data in the released bags is stored as raw packets due to resource constraints on the platforms. To convert these packets into point clouds, we provide ROS1 nodes that can be used to unpack the packets into `sensor_msgs/PointCloud2` messages. The nodes can be used to rewrite the `sensors_only.bag` files to `sensors_only_with_clouds.bag` files containing the point clouds. The nodes are specific for each LiDAR type so please use the appropriate one per platform as per the table in the [README](README.md).
+
+## Ouster OS0-128 (AR-1)
+
+In your catkin workspace,
+
+- Get this repository: `git@github.com:ntnu-arl/ouster-ros.git` on this branch: `feature/rosbag_packet_unpacking`
+- Follow the instructions in its README to build the package and source the workspace.
+- Use `rosrun ouster_ros bag_converter sensors_only.bag sensors_only_with_clouds.bag ouster` to convert the bags.
+
+## RoboSense Airy (UniPilot AR-2 / GR-1)
+
+In your catkin workspace,
+
+- Get this repository: `git@github.com:ntnu-arl/rslidar_sdk.git` on this branch: `develop`
+- Follow the instructions in its README to build the package and source the workspace.
+- Use `rosrun rslidar_sdk bag_converter config/config.yaml sensors_only.bag sensors_only_with_clouds.bag` to convert the bags.
+
+## Hesai JT-128 (UniPilot handheld)
+
+In your catkin workspace,
+- Get this repository: `git@github.com:ntnu-arl/HesaiLidar_ROS_2.0.git` on this branch: `bag_converter`
+- Follow the instructions in its README to build the package and source the workspace.
+- Use `rosrun hesai_ros_driver bag_converter sensors_only.bag sensors_only_with_clouds.bag` to convert the bags.