Robust Stereo Visual Odometry Based on Probabilistic Decoupling Ego-Motion Estimation and 3D SSC

The outliers caused by noise and mismatching severely restrict the precision of visual odometry. Moreover, the dynamic environment is also a crucial element that decreases the robustness of the systems. This paper presents a robust stereo visual odometry by decoupled ego-motion estimation based on probabilistic matches and rejecting the outliers of dynamic objects through motion segmentation. Fast ZNCC method, based on local sum table and partition upper bound schemes, is presented for selecting probabilistic matches while keeping run-time efficiency. The selection of multi-correspondences can avoid mismatching of corresponding points. In consideration of noise interference, the essential matrix is computed in a probabilistic framework to estimate the initial value of the rotation matrix without estimated depth errors involved. Then, in order to estimate pose robustly in dynamic environment, a modified sparse subspace clustering (SSC) method is discussed, which aims to cluster the tracked 3D points cloud to avoid errors caused by affine transformation. The non-negative constraint makes the method suitable for fast moving camera. The proposed 3D-SSC method removes the outliers belonging to dynamic objects effectively. Finally, the detected inliers and depths are employed to estimate the translation matrix and refine rotation matrix. The proposed method is evaluated on the KITTI benchmark and compared with the state-of-the-art methods. The results show that our method is more robust as it can detect outliers more accurately in dynamic environments and achieve higher precision in motion estimation.