Detection and tracking of moving objects in SLAM using vision sensors

This paper presents algorithms for improving the detection of moving objects in robot visual simultaneous localization and mapping (SLAM). The method of speeded-up robust feature (SURF) is employed in the algorithm to provide a robust detection for image features as well as a better description of landmarks in the map of a visual SLAM system. Meanwhile, a moving object detection (MOD) is designed based on the correspondence constraint of the essential matrix for the feature points on image plane. Experiments are carried out on a handheld camera sensor to verify the performances of the proposed algorithms. The results show that the integration of SURF and MOD is efficient to improve the robustness of robot SLAM.