UAV
DR-LRIO degradation datasets
Degradation-resilient odometry
Two public aerial LiDAR, radar and inertial recordings challenge odometry in a self-similar tunnel and a fog-filled corridor.
Access links
Primary places to inspect, download, read about, or reproduce the dataset.
Quick facts
- Vehicle type
- UAV
- Environment
- Indoor
- Data origin
- Real-world
- Sensor count
- 6
- Ground truth
- Not reported
- Calibration
- Parameters Only
- Annotations
- Not reported
Sensor overview
High-level sensor availability before the detailed sensor records below.
Ground truth
Reference scope, method, rate, coverage, and provenance.
Not reported
Calibration and synchronization
Reported calibration level, reproducibility signals, and supporting notes.
- Level
- Parameters Only
- Processed parameters
- Available
- Raw calibration data
- Not reported
- Calibration targets
- Not reported
Parameters only
Processed calibration parameters are reported, but raw calibration data is not reported.
Notes
Author repository supplies CAD-derived LiDAR-to-IMU and radar-to-IMU transforms. Raw calibration sessions are not established.
Known or intentional limitations
Reported constraints and characteristics to check before using the dataset.
The maintained public release lists two bags: a self-similar tunnel flight and a fog-filled corridor flight. Motion-capture arena experiments in the paper do not establish a third publicly released recording or reference file.
IMU/radar trigger timestamps and LiDAR replay require the documented driver handling. Oscillator accuracy is not an independently measured common timestamp-error bound.
CAD extrinsics are documented, while complete raw calibration sessions and independent reference trajectory coverage remain unknown.
Compensated/uncompensated VectorNav topics share one physical IMU. Published topic rates do not establish native accelerometer and gyroscope sampling.
Cameras
Not reported
IMUs
VectorNav VN100
- Model
- VectorNav VN100
- Count
- 1
- Accel / gyro
- Not reported
Ouster integrated IMU packet stream
- Model
- Not reported
- Count
- 1
- Accel / gyro
- Not reported
GNSS
Not reported
LiDAR
Ouster OS0-128
- Model or name
- Ouster OS0-128
- Count
- 1
- Dimensions
- 3
- Rate
- 10 Hz
- Channels
- 128
- Range
- Not reported
Radar
Texas Instruments IWR6843AOP-EVM
- Count
- 1
- Released data
- Point detections / point clouds
- Frame rate
- 10 Hz
- Doppler velocity
- Available
- Elevation angle
- Available
- Configured range
- 15.999 m
- Field of view
- Not reported
- Carrier frequency
- 60 GHz
- Notes
- Configured 60 GHz FMCW chirp; range/Doppler/RCS point observations.
Additional sensors
Barometer
- Name
- VectorNav pressure readings
- Count
- 1
- Notes
- Author topic inventory includes pressure. No component model, physical count or native sample rate is inferred.
Magnetometer
- Name
- VectorNav magnetic-field readings
- Count
- 1
- Notes
- Author topic inventory lists a magnetic-field stream from the navigation unit; physical component model/count and native sample rate are not established.
Annotations
Reported annotation availability and task support.
- Availability
- Not reported
- Format
- Not reported
- Class count
- Not reported
Semantic segmentation
Not reported
Instance segmentation
Not reported
Object detection
Not reported
Optical flow
Not reported
Depth ground truth
Not reported
Citation
Citation key and BibTeX kept at the end of the page for reference.
@inproceedings{nissov2024degradation,
author={Nissov, Morten and Khedekar, Nikhil and Alexis, Kostas},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
title={Degradation Resilient LiDAR-Radar-Inertial Odometry},
year={2024},
pages={8587-8594},
doi={10.1109/ICRA57147.2024.10611444}
}Dataset corrections
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