OSM Guided Pavement Constrained Navigation Using Segmentation-Based Local Control for Sidewalk Inspection Robots

Abstract

Autonomous navigation on narrow pedestrian sidewalks presents a significant challenge for mobile robots used in structural health monitoring (SHM) applications. Accurate global and local perception is required to keep the mobile robot within the sidewalk boundaries while avoiding curbs, vegetation, and dynamic obstacles. This paper presents a lower-cost, LiDAR-free embedded robot control system for accurate sidewalk navigation toward predefined global goals without requiring online mapping or prior SLAM runs. Global navigation references were obtained by converting raw OpenStreetMap (OSM) data into a georeferenced waypoint layer, while local path planning is achieved using a custom lightweight semantic segmentation model integrated with the Robot Operating System 2 (ROS 2) Nav2 navigation stack. The proposed approach was first validated in ROS 2-based simulation and subsequently optimized for real-time deployment on a mobile robot platform equipped with low-cost sensing, including a stereo camera, GPS, and IMU, running on an edge computing system. Experimental results demonstrate stable simulation-based navigation, accurate real-time segmentation performance, and the reliability of the OSM-derived coordinates for sidewalk navigation.