Picking Up Speed: Continuous-Time Lidar-Only Odometry using Doppler Velocity Measurements
| Autor: | Yuchen Wu, David J. Yoon, Keenan Burnett, Soeren Kammel, Yi Chen, Heethesh Vhavle, Timothy D. Barfoot |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Human-Computer Interaction
FOS: Computer and information sciences Computer Science - Robotics Control and Optimization Artificial Intelligence Control and Systems Engineering Mechanical Engineering Biomedical Engineering Computer Vision and Pattern Recognition Robotics (cs.RO) Computer Science Applications |
| Popis: | Frequency-Modulated Continuous-Wave (FMCW) lidar is a recently emerging technology that additionally enables per-return instantaneous relative radial velocity measurements via the Doppler effect. In this letter, we present the first continuous-time lidar-only odometry algorithm using these Doppler velocity measurements from an FMCW lidar to aid odometry in geometrically degenerate environments. We apply an existing continuous-time framework that efficiently estimates the vehicle trajectory using Gaussian process regression to compensate for motion distortion due to the scanning-while-moving nature of any mechanically actuated lidar (FMCW and non-FMCW). We evaluate our proposed algorithm on several real-world datasets, including publicly available ones and datasets we collected. Our algorithm outperforms the only existing method that also uses Doppler velocity measurements, and we study difficult conditions where including this extra information greatly improves performance. We additionally demonstrate state-of-the-art performance of lidar-only odometry with and without using Doppler velocity measurements in nominal conditions. Code for this project can be found at: https://github.com/utiasASRL/steam_icp. RA-L & ICRA2023 |
| Databáze: | OpenAIRE |
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