Enhanced Indoor Localization Technique Based on Point Cloud Conversion Image Matching

It is important that indoor autonomous mobile platforms have the capability of localization in general indoor environments. In this study, using multi-threaded vehicle-mounted light detection and ranging (LiDAR), we conducted indoor autonomous mobile platform localization experiments based on a point cloud conversion 2D image method with an interpolated probability distribution, performed a scan matching analysis by converting 2D images based on an interpolated probability distribution while using occupied grid maps, and introduced a multi -resolution map method to avoid falling into a local optimum. We found that the method adopted in this study achieves a higher indoor positioning accuracy and a higher matching speed with reduced computational effort while avoiding local optima. Compared with other traditional indoor positioning methods, this method has the advantages of universal applicability and robustness against signal interference and other problems.