This paper introduces an approach that enhances the state estimator for high-speed autonomous race cars, addressing challenges from unreliable measurements, localization failures, and computing resource management. The proposed robust localization system utilizes a Bayesian-based probabilistic approach to evaluate multimodal measurements, ensuring the use of credible data for accurate and reliable localization, even in harsh racing conditions. To tackle potential localization failures, we present a resilient navigation system which enables the race car to continue track-following by leveraging direct perception information in planning and execution, ensuring continuous performance despite localization disruptions. In addition, efficient computing is critical to avoid overload and system failure. Hence, we optimize computing resources using an efficient LiDAR-based state estimation method. Leveraging CUDA programming and GPU acceleration, we perform nearest points search and covariance computation efficiently, overcoming CPU bottlenecks. Simulation and real-world tests validate the system's performance and resilience. The proposed approach successfully recovers from failures, effectively preventing accidents and ensuring safety of the car.

翻译：本文提出了一种增强高速自主赛车状态估计器的方法，解决了不可靠测量、定位失败和计算资源管理带来的挑战。所提出的鲁棒定位系统采用基于贝叶斯的概率方法来评估多模态测量值，确保在恶劣赛车条件下也能使用可信数据进行准确可靠的定位。为解决潜在的定位失败问题，我们提出了一种弹性导航系统，通过利用规划与执行过程中的直接感知信息使赛车能够继续沿赛道行驶，确保在定位中断时仍能保持持续性能。此外，高效计算对于避免过载和系统故障至关重要。因此，我们采用基于激光雷达的高效状态估计方法优化计算资源。利用CUDA编程与GPU加速，我们高效执行最近点搜索和协方差计算，克服了CPU瓶颈。仿真与实车测试验证了系统的性能与弹性。所提出的方法成功从失败中恢复，有效防止事故并确保车辆安全。