单目摄像头实现高精度低延迟赛车位姿估计，适合边缘部署。
SPARK: Low Latency Single-Camera 3D Pose Estimation for Autonomous Racing using Keypoints
- 基于优化的YOLO模型与赛道固定几何结构，实现快速关键点检测。
- 在真实赛道数据上达到优于主流单目方法的精度，延迟更低。
- 适合需要低延迟感知的自动驾驶竞速场景，资源占用少。
在自动驾驶竞速中，需快速检测其他参赛者运动以规划安全、无碰撞轨迹。相比视觉方法，激光雷达检测速度慢且难以部署于边缘设备，导致高速动态操控时检测延迟，限制追踪性能。采用单目3D检测可实现易于部署、低延迟的目标检测。本文提出SPARK，一种基于关键点检测的单摄像头赛车位姿估计算法，在保持低延迟和低资源消耗的同时，实现远距离高精度检测，性能超越现有单目相机检测算法。通过使用优化的YOLO模型并利用自动驾驶竞速场景中的固定几何结构，该算法在真实赛道数据上的表现优于当前最先进的激光雷达和相机检测方法。代码已开源：https://github.com/TUMFTM/SPARK-camera-det
原文摘要 · Abstract (English)
In autonomous racing, fast detection of other participants' movements is required to plan safe, collision-free trajectories with non-cooperative opponents. LiDAR detection is inherently slower and harder to deploy on edge devices than vision methods, causing delayed detections that limit object tracking performance during high-dynamic maneuvering. Utilizing monocular 3D detection enables an easy-to-deploy, low-latency detection of other participants on the racetrack. We present SPARK, a single-camera pose-estimation algorithm for autonomous racing using keypoint detection. It achieves long-range detection with high accuracy, exceeding the performance of state-of-the-art monocular camera detection algorithms while maintaining lower latency. By employing well-optimized YOLO models and leveraging the fixed geometry in the autonomous racing domain, the algorithm also exhibits low latency and resource usage. We evaluate the performance of our approach on real-world autonomous racing data and compare it to state-of-the-art LiDAR and camera detection algorithms. The source code is available at: https://github.com/TUMFTM/SPARK-camera-det
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