Spatiotemporal 3D motion vector filtering method for robust visual odometry

Abstract

Most of the previous visual odometry methods cannot deal with a large independently moving object that takes up over 50% of the image area. To overcome this problem, the spatiotemporal filter is incorporated into the RANSAC method to filter out false match that occurrs by a large independently moving object. This spatiotemporal filter uses the current and previous motion vector's length and direction. Experimental results demonstrate that the proposed method effectively rejects the motion vectors generated from large independently moving objects and improves the visual odometry accuracy.

Study on Optimization Method of Visual Odometry Based on Feature Matching

“…By verifying the correctness of the model, the mismatched feature points can be precisely fltered out. Te motion model of transformation between images is shown in equation (3). Te relative transformation matrix H has 8 degrees of freedom, which can be calculated and solved by 4 pairs of corresponding feature points.…”

“…VO can provide key pose information for robot localization, mapping, and navigation. However, when the sensor is afected by factors such as environment, illumination, and moving objects [3], the feature vectors of image feature points will change greatly, resulting in mismatches in the matching candidate set, thus afecting the robot pose estimation. Terefore, how to eliminate mismatched feature points is a key issue in VO and an important prerequisite for pose estimation.…”