用三帧激光扫描拼成彩色图，实现嵌入式设备实时精准避障。
Real-Time 2D LiDAR Object Detection Using Three-Frame RGB Scan Encoding
- 三帧激光数据转为伪彩色图输入YOLOv8n
- 模拟环境中达98.4%检测精度，延迟仅47.8毫秒
- 无需摄像头，适合隐私敏感的机器人应用
室内服务机器人需要鲁棒、更隐私友好且能在嵌入式设备上运行的感知能力。本文提出一种无摄像头的2D LiDAR目标检测流程，通过将连续三帧扫描数据作为RGB通道堆叠，生成紧凑的YOLOv8n输入，无需构建占据网格，同时保留角度结构和运动线索。在Webots中160个随机化室内场景下严格场景级留出评估，该方法在四个物体类别上达到98.4% [email protected]（0.778 [email protected]:0.95），精确率94.9%，召回率94.7%。在Raspberry Pi 5上实现实时运行，平均预热后端到端延迟为47.8ms/帧，包括扫描编码与后处理。相比同平台报告的占用网格LiDAR-YOLO方案，本方法端到端延迟显著更低。尽管结果基于仿真，但表明轻量级时序编码可使纯激光雷达在嵌入式室内机器人上实现高精度、实时检测，无需捕捉可见光外观。
原文摘要 · Abstract (English)
Indoor service robots need perception that is robust, more privacy-friendly than RGB video, and feasible on embedded hardware. We present a camera-free 2D LiDAR object detection pipeline that encodes short-term temporal context by stacking three consecutive scans as RGB channels, yielding a compact YOLOv8n input without occupancy-grid construction while preserving angular structure and motion cues. Evaluated in Webots across 160 randomized indoor scenarios with strict scenario-level holdout, the method achieves 98.4% [email protected] (0.778 [email protected]:0.95) with 94.9% precision and 94.7% recall on four object classes. On a Raspberry Pi 5, it runs in real time with a mean post-warm-up end-to-end latency of 47.8ms per frame, including scan encoding and postprocessing. Relative to a closely related occupancy-grid LiDAR-YOLO pipeline reported on the same platform, the proposed representation is associated with substantially lower reported end-to-end latency. Although results are simulation-based, they suggest that lightweight temporal encoding can enable accurate and real-time LiDAR-only detection for embedded indoor robotics without capturing RGB appearance.
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