SLAM-driven intelligent autonomous mobile robot navigation for construction applications

Abstract

The demand for construction site automation with mobile robots is increasing due to its advantages in potential cost-saving, productivity, and safety. To be realistically deployed in construction sites, mobile robots must be capable of navigating in unstructured and cluttered environments. Furthermore, mobile robots should recognize both static and dynamic obstacles to determine drivable paths. However, existing robot navigation methods are not suitable for construction applications due to the challenging environmental conditions in construction sites. This study introduces an autonomous as-is 3D spatial data collection and perception method for mobile robots specifically aimed for construction job sites with many spatial uncertainties. The proposed Simultaneous Localization and Mapping (SLAM)-based navigation and object recognition methods were implemented and tested with a custom-designed mobile robot platform, Ground Robot for Mapping Infrastructure (GRoMI), which uses multiple laser scanners and a camera to sense and build a 3D environment map. Since SLAM did not detect uneven surface conditions and spatiotemporal objects on the ground, an obstacle detection algorithm was developed to recognize and avoid obstacles and the highly uneven terrain in real time. Given the 3D real-time scan map generated by 3D laser scanners, a path-finding algorithm was developed for autonomous navigation in an unknown environment with obstacles. Overall, the 3D color-mapped point clouds of construction sites generated by GRoMI were of sufficient quality to be used for many construction management applications such as construction progress monitoring, safety hazard identification, and defect detection.