RGB-D Camera and 2D Laser Integration for Robot Navigation in Dynamic Environments

Navigation, localization, and mapping are challenging tasks that any mobile service robot needs to solve. Given that this type of robots generally navigate in 2D planar environments, a common and highly effective solution is laser-based mapping (SLAM) and navigation. Unfortunately, due to their incapability to detect obstacles outside of a single plane view, these algorithms are affected by irregular obstacles in the environment; even more when there are dynamic obstacles. To address this problem, we propose a method to integrate data from a 2D laser range finder (LRF) and an RGB-D camera. In this paper, our goal is to enrich a 2D grid-based map by extracting and processing a depth image from an RGB-D camera, fusing this with the information from the LRF. To test the algorithm, we set up five different scenarios in which pure laser navigation would be an ambitious task. Comparative results between pure LRF and LRF + RGB-D navigation are presented. In spite of the simplicity of the method, results show a significant improvement in the robot’s navigation, making it more robust in complex, dynamic environments.