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

2026-02-02

AI Summary

This work addresses the urgent need for efficient, privacy-preserving real-time perception in indoor service robots on embedded platforms. To overcome the privacy risks of conventional RGB-based approaches and the high computational cost or temporal information neglect of existing LiDAR methods, the authors propose a purely 2D LiDAR-based object detection framework. By encoding three consecutive LiDAR scans into an image-like RGB representation that preserves angular structure and motion cues, the method directly feeds this input into a lightweight YOLOv8n model—bypassing occupancy grid construction entirely. Evaluated in Webots across 160 random indoor scenes, the approach achieves 98.4% mAP@0.5 and 77.8% mAP@0.5:0.95, with both precision and recall exceeding 94.7%. On a Raspberry Pi 5, it attains an end-to-end latency of only 47.8 ms, significantly outperforming existing grid-based methods.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Object Detection & Categorization
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

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.

Problem

• 2D LiDAR
• object detection
• indoor robotics
• real-time perception
• privacy-friendly sensing

Innovation

• LiDAR object detection
• temporal encoding
• YOLOv8
• embedded robotics
• scan-to-RGB encoding