A novel and effective moving-objects detection method combined with stereo localization and mapping system

Abstract

In this paper, we propose a novel moving-objects detection method which, in contrast to state-of-the-art moving-objects detection methods, takes static feature points into consideration during detection. It benefits both the tracking and mapping approaches in real-time Simultaneous Localization and Mapping (SLAM) system whose localization depends on static objects primarily. Our method obtains accurate static feature point sets continuously to estimate camera poses. In return, these camera poses are employed to find inliers and outliers using succinct distinction method. More specifically, we estimate camera pose by using static map points extracted from a high accurate global map. The map is generated in a real-time stereo SLAM system, and the camera pose is estimated and optimized by using a 3D-2D projection matching search with local bundle adjustment optimization. Once we have a calculated camera pose, we begin to find inliers and outliers. Finally, combined with superpixel segmentation, we capture the moving objects and then give feedback to the whole SLAM system. Detailed results are demonstrated comparing to other moving-objects detection methods on selected KITTI datasets with moving objects.