Static object imaging features recognition algorithm in dynamic scene mapping

In dynamic scene monocular visual SLAM, it is important to recognize static object imaging features for mapping. The optical flow of noise and the object moving in the same orientations would have the same orientations of static feature for two frames image, that would disturb static feature point recognition. Because the translational motion optical flow orientations method judges with optical flow of single feature point and translational motion orientations, it is not reliable for static feature point recognition. Therefore, a 3D motion segmentation combined for static object imaging features recognition is proposed, which clusters the same optical flow of feature points in 3D motion subspace, including orientations and amplitude of translation and rotation. The simulation results show that the proposed static object imaging features recognition method improves the recognition correct rate and reliability of static features. So, it is effective for static object imaging features recognition in dynamic scene mapping.