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.

翻译：本文介绍SynPF，一种针对高速赛车环境定制的基于MCL的算法。通过与当前最先进的位姿图SLAM算法Cartographer进行基准对比，SynPF借鉴并融合了先前粒子滤波方法的协同效应，专为高性能赛车领域设计。我们广泛的实地评估表明，Cartographer在标称条件下表现出色，但在遭遇车轮打滑（赛车场景中因抓地力变化和激进驾驶行为而常见的一种现象）时则显得力不从心。相比之下，SynPF在这些挑战性条件下展现出鲁棒性，并在无GPU的车载计算机上实现了1.25毫秒的低延迟计算时间。本研究利用F1TENTH平台（一种1:10比例的自主赛车）不仅揭示了现有算法在高达7.6米/秒高速场景下的脆弱性，还强调了SynPF作为可行替代方案的潜力，尤其是在里程计条件恶化的情况下。