SIIS-SLAM: A Vision SLAM Based on Sequential Image Instance Segmentation

Abstract

Simultaneous localization and mapping (SLAM) is a fundamental function of intelligent robots. To reduce the influence of dynamic objects on SLAM in dynamic environments, this study pro-poses a visual SLAM based on sequential image segmentation, referred to as SIIS-SLAM. Based on ORB-SLAM3, SIIS-SLAM integrates the sequential image instance segmentation and optical flow dynamic detection module. The sequential image segmentation module is designed to eliminate the effectiveness of dynamic objects in the estimation of relative pose between sequential frames. Specifically, based on the coarse relative pose estimated by ORB-SLAM3 and the box coordinates of instances detected by Mask R-CNN, the sequential image segmentation module effectively improves the speed and accuracy of instance segmentation. Dynamic objects can be effectively detected by combining the instance segmentation results and optical flow module. Filtering the feature points in dynamic objects can improve the accuracy and robustness of SLAM. Experimental results demonstrate that SIIS-SLAM achieves the better accuracy in dynamic environments compared to ORB SLAM3 and other advanced methods.