This paper presents an algorithm for moving object detection (MOD) in robot visual simultaneous localization and mapping (SLAM). The algorithm is designed based on the defining epipolar constraint for the corresponding feature points on image plane. An essential matrix obtained using the state estimator is utilized to represent the pipolar constraint. Meanwhile, 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 and of moving objects in visual SLAM system. Experiment is carried out on a hand-held monocular camera to validate the performances of the proposed algorithm. The results show that the integration of MOD and SURF is efficient for
robot navigating in dynamic environments.
Relation:
Intelligent Autonomous Systems 12: Proceedings of the 12th International Conference, pp.183-192
