Real-Time 2D LiDAR Object Detection Using Three-Frame RGB Scan Encoding

TL;DR

This paper introduces a real-time, camera-free 2D LiDAR object detection method that encodes short-term temporal context using three consecutive scans as RGB channels, achieving high accuracy and efficiency on embedded hardware.

Contribution

It presents a novel three-frame scan encoding technique for LiDAR data that enables accurate, real-time object detection without occupancy-grid construction on embedded devices.

Findings

Achieves 98.4% mAP@0.5 in indoor scenarios

Runs in 47.8ms per frame on Raspberry Pi 5

Outperforms occupancy-grid based methods in latency

Abstract

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% mAP@0.5 (0.778 mAP@0.5:0.95) 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.