Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 170 tok/s
Gemini 2.5 Pro 50 tok/s Pro
GPT-5 Medium 30 tok/s Pro
GPT-5 High 41 tok/s Pro
GPT-4o 60 tok/s Pro
Kimi K2 208 tok/s Pro
GPT OSS 120B 440 tok/s Pro
Claude Sonnet 4.5 35 tok/s Pro
2000 character limit reached

2D Ego-Motion with Yaw Estimation using Only mmWave Radars via Two-Way weighted ICP (2404.00830v1)

Published 31 Mar 2024 in cs.RO

Abstract: The interest in single-chip mmWave Radar is driven by their compact form factor, cost-effectiveness, and robustness under harsh environmental conditions. Despite its promising attributes, the principal limitation of mmWave radar lies in its capacity for autonomous yaw rate estimation. Conventional solutions have often resorted to integrating inertial measurement unit (IMU) or deploying multiple radar units to circumvent this shortcoming. This paper introduces an innovative methodology for two-dimensional ego-motion estimation, focusing on yaw rate deduction, utilizing solely mmWave radar sensors. By applying a weighted Iterated Closest Point (ICP) algorithm to register processed points derived from heatmap data, our method facilitates 2D ego-motion estimation devoid of prior information. Through experimental validation, we verified the effectiveness and promise of our technique for ego-motion estimation using exclusively radar data.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (11)
  1. Y. Zhou, L. Liu, H. Zhao, M. López-Benítez, L. Yu, and Y. Yue, “Towards deep radar perception for autonomous driving: Datasets, methods, and challenges,” Sensors, vol. 22, no. 11, p. 4208, 2022.
  2. D. Kellner, M. Barjenbruch, J. Klappstein, J. Dickmann, and K. Dietmayer, “Instantaneous ego-motion estimation using multiple doppler radars,” in 2014 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2014, pp. 1592–1597.
  3. Y. S. Park, Y.-S. Shin, J. Kim, and A. Kim, “3d ego-motion estimation using low-cost mmwave radars via radar velocity factor for pose-graph slam,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7691–7698, 2021.
  4. 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.
  5. 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.
  6. Y. Almalioglu, M. Turan, C. X. Lu, N. Trigoni, and A. Markham, “Milli-rio: Ego-motion estimation with low-cost millimetre-wave radar,” IEEE Sensors Journal, vol. 21, no. 3, pp. 3314–3323, 2020.
  7. P. K. Rai, N. Strokina, and R. Ghabcheloo, “4dego: ego-velocity estimation from high-resolution radar data,” Frontiers in Signal Processing, vol. 3, p. 1198205, 2023.
  8. P. Zhao, C. X. Lu, B. Wang, N. Trigoni, and A. Markham, “3d motion capture of an unmodified drone with single-chip millimeter wave radar,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 5186–5192.
  9. F. HM, “Adaptive detection mode with threshold control as a function of spatially sampled clutter-level estimates,” Rca Rev., vol. 29, pp. 414–465, 1968.
  10. P. Bergström and O. Edlund, “Robust registration of point sets using iteratively reweighted least squares,” Computational optimization and applications, vol. 58, pp. 543–561, 2014.
  11. A. Kramer, K. Harlow, C. Williams, and C. Heckman, “Coloradar: The direct 3d millimeter wave radar dataset,” The International Journal of Robotics Research, vol. 41, no. 4, pp. 351–360, 2022.

Summary

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

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.