A Robust Visual SLAM System for Small-Scale Quadruped Robots in Dynamic Environments

This paper presents a robust visual SLAM system designed for small-scale quadruped robots (ViQu-SLAM) for accurate localization, especially to mitigate the issue of erroneous data association caused by moving objects in dynamic environments. The proposed approach leverages a selfadaptive framework that integrates semantic segmentation with alterations in the spatial location of categorized map points. Besides, combination of leg odometry derived from forward kinematics with IMU provides scale information for positional transformations between keyframes, thus optimizing the overall localization accuracy of quadruped robots. At last, we performed evaluation across various stages and the results demonstrate competitive performance, with 53.16% reduction in average absolute trajectory error compared to that of ORB-SLAM3 in dynamic benchmark datasets. As a result, ViQu-SLAM, including visual and IMU-fused leg odometry, exhibits promising results on a small quadruped robot, reducing positioning errors in dynamic scenes by an average of 29.36% compared to existing state-of-the-art methods.