Improved ORB-SLAM3 Based on MCMCC for Mobile Robot Navigation

Abstract

Purposes The sparse feature map established by using VSLAM cannot be directly used for navigation, and the existing methods of converting sparse feature maps into grid maps for navigation suffer from low accuracy of map construction, resulting in the failure to achieve accurate navigation. An improved ORB-SLAM3 navigation method is proposed based on multi-convex combined maximum correntropy criterion (MCMCC), which can realize the construction, positioning, and navigation of indoor real-time mapping on the embedded device with only a RGB-D camera. Methods The proposed method uses the back-end optimization algorithm based on MCMCC to combat the influence of non-Gaussian noises and to further improve the mapping accuracy of ORB-SLAM. Subsequently, in the process of converting the sparse feature map into a grid map, the grid map is updated with the loop detection information to improve the grid map accuracy for accurate navigation. Findings Furthermore, simulation and real scene experiments have been carried out to verify the effectiveness of the proposed method.

Keywords

• vslam
• back-end optimization
• mcmcc
• raster map
• loop renewal
• navigation