轻量级激光雷达锥形检测框架使用随机森林
Lightweight LiDAR-Based Cone Detection Framework Using Random Forest for Formula Student Driverless
这个论文分享了一个用随机森林做的轻量级激光雷达锥形检测方案，在CPU上运行很快，对资源有限的无人车团队可能有参考价值。
这篇论文提出了一种专为CPU执行的轻量级激光雷达感知管道，用于Formula Student无人驾驶车辆。它结合了地面移除、IMU运动补偿、DBSCAN聚类和基于几何特征的随机森林分类。特征重要性分析将模型输入从12个特征减少到7个，同时保持性能。在来自真实FSD赛事的2,371个标注簇上评估，该管道在仅CPU硬件上实现98.33%的F1分数和3.13毫秒的端到端运行时间。
Lightweight LiDAR-Based Cone Detection Framework Using Random Forest for Formula Student Driverless
Reliable, low-latency perception is crucial for Formula Student Driverless vehicles, yet many existing pipelines rely on deep learning and multi-sensor fusion, often requiring GPU acceleration. This paper presents a lightweight LiDAR-only perception pipeline tailored for CPU execution, combining ground removal, IMU-based motion compensation, DBSCAN clustering, and geometric feature-based Random Forest classification. Feature importance analysis reduced the model input from 12 to 7 features while preserving performance. Evaluated on 2,371 labeled clusters collected from real FSD events, the pipeline achieves an F1-score of 98.33% and an end-to-end runtime of 3.13 ms on CPU-only hardware. The released dataset, labeling tool, and trained models provide a practical and reproducible baseline for other resource-constrained autonomous racing teams.