TY - GEN
T1 - OdomBeyondVision:
T2 - An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum
AU - Li, Peize
AU - Cai, Kaiwen
AU - Saputra, Muhamad Risqi U.
AU - Dai, Zhuangzhuang
AU - Lu, Chris Xiaoxuan
PY - 2022/10/23
Y1 - 2022/10/23
N2 - This paper presents a multimodal indoor odometry dataset, OdomBeyondVision, featuring multiple sensors across the different spectrum and collected with different mobile platforms. Not only does OdomBeyondVision contain the traditional navigation sensors, sensors such as IMUs, mechanical LiDAR, RGBD camera, it also includes several emerging sensors such as the single-chip mmWave radar, LWIR thermal camera and solid-state LiDAR. With the above sensors on UAV, UGV and handheld platforms, we respectively recorded the multimodal odometry data and their movement trajectories in various indoor scenes and different illumination conditions. We release the exemplar radar, radar-inertial and thermal-inertial odometry implementations to demonstrate their results for future works to compare against and improve upon. The full dataset including toolkit and documentation is publicly available at: https://github.com/MAPS-Lab/OdomBeyondVision.
AB - This paper presents a multimodal indoor odometry dataset, OdomBeyondVision, featuring multiple sensors across the different spectrum and collected with different mobile platforms. Not only does OdomBeyondVision contain the traditional navigation sensors, sensors such as IMUs, mechanical LiDAR, RGBD camera, it also includes several emerging sensors such as the single-chip mmWave radar, LWIR thermal camera and solid-state LiDAR. With the above sensors on UAV, UGV and handheld platforms, we respectively recorded the multimodal odometry data and their movement trajectories in various indoor scenes and different illumination conditions. We release the exemplar radar, radar-inertial and thermal-inertial odometry implementations to demonstrate their results for future works to compare against and improve upon. The full dataset including toolkit and documentation is publicly available at: https://github.com/MAPS-Lab/OdomBeyondVision.
UR - https://ieeexplore.ieee.org/abstract/document/9981865
U2 - 10.1109/IROS47612.2022.9981865
DO - 10.1109/IROS47612.2022.9981865
M3 - Conference publication
BT - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PB - IEEE
ER -