Optimization of ORB-SLAM3 algorithm based on deep learning

A dynamic feature removal method combining the deep learning network YOLOv5 (You Only Look Once Version 5) and geometric constraints was proposed to address the poor positioning accuracy and robustness of the current ORB-SLAM3 system in dynamic scenarios, which could not meet the positioning needs of mobile robots in such scenarios. A dynamic feature point detection and removal thread was embedded in the tracking thread to eliminate the impact of dynamic feature points on camera pose estimation, thereby improving the accuracy and robustness of the SLAM system. The experiment used publicly available TUM datasets to validate the improved algorithm, and the experimental results showed that the improved algorithm had significant improvements in localization accuracy and robustness.