Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Modeling Point Uncertainty in Radar SLAM (2402.16082v1)

Published 25 Feb 2024 in cs.RO

Abstract: While visual and laser-based simultaneous localization and mapping (SLAM) techniques have gained significant attention, radar SLAM remains a robust option for challenging conditions. This paper aims to improve the performance of radar SLAM by modeling point uncertainty. The basic SLAM system is a radar-inertial odometry (RIO) system that leverages velocity-aided radar points and high-frequency inertial measurements. We first propose to model the uncertainty of radar points in polar coordinates by considering the nature of radar sensing. Then in the SLAM system, the uncertainty model is designed into the data association module and is incorporated to weight the motion estimation. Real-world experiments on public and self-collected datasets validate the effectiveness of the proposed models and approaches. The findings highlight the potential of incorporating radar point uncertainty modeling to improve the radar SLAM system in adverse environments.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (26)
  1. T. Qin, P. Li, and S. Shen, “Vins-mono: A robust and versatile monocular visual-inertial state estimator,” IEEE Transactions on Robotics, vol. 34, no. 4, pp. 1004–1020, 2018.
  2. B. Jiang and S. Shen, “A lidar-inertial odometry with principled uncertainty modeling,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 13 292–13 299.
  3. Z. Hong, Y. Petillot, A. Wallace, and S. Wang, “Radarslam: A robust simultaneous localization and mapping system for all weather conditions,” The International Journal of Robotics Research, vol. 41, no. 5, pp. 519–542, 2022.
  4. H. Yin, R. Chen, Y. Wang, and R. Xiong, “Rall: end-to-end radar localization on lidar map using differentiable measurement model,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 6737–6750, 2021.
  5. A. Kramer, C. Stahoviak, A. Santamaria-Navarro, A.-a. Agha-mohammadi, and C. Heckman, “Radar-inertial ego-velocity estimation for visually degraded environments,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 5739–5746.
  6. Y. Zhuang, B. Wang, J. Huai, and M. Li, “4d iriom: 4d imaging radar inertial odometry and mapping,” IEEE Robotics and Automation Letters, 2023.
  7. Q. Huang, Y. Liang, Z. Qiao, S. Shen, and H. Yin, “Less is more: Physical-enhanced radar-inertial odometry,” in 2024 IEEE international conference on robotics and automation (ICRA).   IEEE, 2024.
  8. A. Kramer, K. Harlow, C. Williams, and C. Heckman, “Coloradar: The direct 3d millimeter wave radar dataset,” 2021.
  9. M. L. Rodríguez-Arévalo, J. Neira, and J. A. Castellanos, “On the importance of uncertainty representation in active slam,” IEEE Transactions on Robotics, vol. 34, no. 3, pp. 829–834, 2018.
  10. H. Yin, S. Li, Y. Tao, J. Guo, and B. Huang, “Dynam-slam: An accurate, robust stereo visual-inertial slam method in dynamic environments,” IEEE Transactions on Robotics, vol. 39, no. 1, pp. 289–308, 2022.
  11. W. Zhen, Y. Hu, J. Liu, and S. Scherer, “A joint optimization approach of lidar-camera fusion for accurate dense 3-d reconstructions,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3585–3592, 2019.
  12. D. Belter, M. Nowicki, and P. Skrzypczyński, “Improving accuracy of feature-based rgb-d slam by modeling spatial uncertainty of point features,” in 2016 IEEE international conference on robotics and automation (ICRA).   IEEE, 2016, pp. 1279–1284.
  13. S. Thrun, “Probabilistic robotics,” Communications of the ACM, vol. 45, no. 3, pp. 52–57, 2002.
  14. M. Shan, Q. Feng, and N. Atanasov, “Orcvio: Object residual constrained visual-inertial odometry,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2020, pp. 5104–5111.
  15. N. Merrill, Y. Guo, X. Zuo, X. Huang, S. Leutenegger, X. Peng, L. Ren, and G. Huang, “Symmetry and uncertainty-aware object slam for 6dof object pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 14 901–14 910.
  16. C. Yuan, X. Liu, X. Hong, and F. Zhang, “Pixel-level extrinsic self calibration of high resolution lidar and camera in targetless environments,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7517–7524, 2021.
  17. S. Clark and G. Dissanayake, “Simultaneous localization and map building using millimeter wave radar to extract natural features,” in Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C), vol. 2.   IEEE, 1999, pp. 1316–1321.
  18. C. Doer and G. F. Trommer, “An ekf based approach to radar inertial odometry,” in 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).   IEEE, 2020, pp. 152–159.
  19. J. Michalczyk, R. Jung, and S. Weiss, “Tightly-coupled ekf-based radar-inertial odometry,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 12 336–12 343.
  20. J. Michalczyk, R. Jung, C. Brommer, and S. Weiss, “Multi-state tightly-coupled ekf-based radar-inertial odometry with persistent landmarks,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 4011–4017.
  21. C. X. Lu, M. R. U. Saputra, P. Zhao, Y. Almalioglu, P. P. De Gusmao, C. Chen, K. Sun, N. Trigoni, and A. Markham, “milliego: single-chip mmwave radar aided egomotion estimation via deep sensor fusion,” in Proceedings of the 18th Conference on Embedded Networked Sensor Systems, 2020, pp. 109–122.
  22. K. Retan, F. Loshaj, and M. Heizmann, “Radar odometry on se(3) with constant acceleration motion prior and polar measurement model,” 2022.
  23. D. He, W. Xu, and F. Zhang, “Kalman filters on differentiable manifolds,” arXiv preprint arXiv:2102.03804, 2021.
  24. F. Pomerleau, F. Colas, R. Siegwart et al., “A review of point cloud registration algorithms for mobile robotics,” Foundations and Trends® in Robotics, vol. 4, no. 1, pp. 1–104, 2015.
  25. J. Zhang, H. Zhuge, Z. Wu, G. Peng, M. Wen, Y. Liu, and D. Wang, “4dradarslam: A 4d imaging radar slam system for large-scale environments based on pose graph optimization,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 8333–8340.
  26. K. Harlow, H. Jang, T. D. Barfoot, A. Kim, and C. Heckman, “A new wave in robotics: Survey on recent mmwave radar applications in robotics,” arXiv preprint arXiv:2305.01135, 2023.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Yang Xu (277 papers)
  2. Qiucan Huang (2 papers)
  3. Shaojie Shen (121 papers)
  4. Huan Yin (29 papers)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now