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Robustness Evaluation of Localization Techniques for Autonomous Racing

Tian Yi Lim, Edoardo Ghignone, Nicolas Baumann, Michele Magno

Abstract

This work introduces SynPF, an MCL-based algorithm tailored for high-speed racing environments. Benchmarked against Cartographer, a state-of-the-art pose-graph SLAM algorithm, SynPF leverages synergies from previous particle-filtering methods and synthesizes them for the high-performance racing domain. Our extensive in-field evaluations reveal that while Cartographer excels under nominal conditions, it struggles when subjected to wheel-slip, a common phenomenon in a racing scenario due to varying grip levels and aggressive driving behaviour. Conversely, SynPF demonstrates robustness in these challenging conditions and a low-latency computation time of 1.25 ms on on-board computers without a GPU. Using the F1TENTH platform, a 1:10 scaled autonomous racing vehicle, this work not only highlights the vulnerabilities of existing algorithms in high-speed scenarios, tested up until 7.6 m/s, but also emphasizes the potential of SynPF as a viable alternative, especially in deteriorating odometry conditions.

Robustness Evaluation of Localization Techniques for Autonomous Racing

Abstract

This work introduces SynPF, an MCL-based algorithm tailored for high-speed racing environments. Benchmarked against Cartographer, a state-of-the-art pose-graph SLAM algorithm, SynPF leverages synergies from previous particle-filtering methods and synthesizes them for the high-performance racing domain. Our extensive in-field evaluations reveal that while Cartographer excels under nominal conditions, it struggles when subjected to wheel-slip, a common phenomenon in a racing scenario due to varying grip levels and aggressive driving behaviour. Conversely, SynPF demonstrates robustness in these challenging conditions and a low-latency computation time of 1.25 ms on on-board computers without a GPU. Using the F1TENTH platform, a 1:10 scaled autonomous racing vehicle, this work not only highlights the vulnerabilities of existing algorithms in high-speed scenarios, tested up until 7.6 m/s, but also emphasizes the potential of SynPF as a viable alternative, especially in deteriorating odometry conditions.
Paper Structure (4 sections, 2 figures, 1 table)

This paper contains 4 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. SynPF Implementation Details
  3. Experimental Results
  4. Conclusion

Figures (2)

  • Figure 1: Comparison of poses generated by diff-drive probabilisticrobotics and TUM motion models ros-high-speed. The left figure shows that at slow speeds, the two models are very similar. The right figure highlights how the TUM motion model further accounts for the reduced steering capacity at higher speeds.
  • Figure 2: Test track used for quantitative localization accuracy evaluations. Left: Utilized test track, featuring grippy and high-quality odometry. Right: Racecar with taped and slippery tires, featuring low-quality odometry on the same test track.