Incremental 2D Grid Map Generation from RGB-D Images

Abstract

In this paper, we extend RGB-D SLAM to address the problem that sparse map-building RGB-D SLAM cannot directly generate maps for indoor navigation and propose a SLAM system for fast generation of indoor planar maps. The system uses RGBD images to generate positional information while converting the corresponding RGBD images into 2D planar lasers for 2D grid navigation map reconstruction of indoor scenes under the condition of limited computational resources, solving the problem that the sparse point cloud maps generated by RGB-D SLAM cannot be directly used for navigation. Meanwhile, the pose information provided by RGB-D SLAM and scan matching respectively is fused to obtain a more accurate and robust pose, which improves the accuracy of map building. Furthermore, we demonstrate the function of the proposed system on the ICL indoor dataset and evaluate the performance of different RGB-D SLAM. The method proposed in this paper can be generalized to RGB-D SLAM algorithms, and the accuracy of map building will be further improved with the development of RGB-D SLAM algorithms.