用三角剖分避开激光雷达陷阱，实现超快自主赛车控制
DTR: Delaunay Triangulation-based Racing for Scaled Autonomous Racing
- 基于激光雷达点云做狄利克雷三角剖分，自动提取赛道中心线
- 相比传统方法提速70%，接近依赖地图的高性能方案
- 延迟仅8.95毫秒，适合嵌入式实时部署
自主赛车中的反应式控制器通过直接将传感器输入映射为控制动作，避免了定位与规划的计算开销。常用方法FTG通过识别激光雷达测距中的间隙并朝选定间隙转向，但在边界不完整或存在死胡同场景下会失效，产生所谓FTG陷阱。本文提出DTR，结合原始激光雷达数据的狄利克雷三角剖分与赛道边界分割，提取中心线并系统规避FTG陷阱。相比FTG，DTR在赛道上实现了70%更快的圈速，接近依赖地图的方法性能。系统延迟仅为8.95毫秒，占用机器人主控单元（OBC）CPU仅38.85%，具备实时性，已在实际环境中成功部署并验证。
原文摘要 · Abstract (English)
Reactive controllers for autonomous racing avoid the computational overhead of full ee-Think-Act autonomy stacks by directly mapping sensor input to control actions, eliminating the need for localization and planning. A widely used reactive strategy is FTG, which identifies gaps in LiDAR range measurements and steers toward a chosen one. While effective on fully bounded circuits, FTG fails in scenarios with incomplete boundaries and is prone to driving into dead-ends, known as FTG-traps. This work presents DTR, a reactive controller that combines Delaunay triangulation, from raw LiDAR readings, with track boundary segmentation to extract a centerline while systematically avoiding FTG-traps. Compared to FTG, the proposed method achieves lap times that are 70\% faster and approaches the performance of map-dependent methods. With a latency of 8.95 ms and CPU usage of only 38.85\% on the robot's OBC, DTR is real-time capable and has been successfully deployed and evaluated in field experiments.
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