Evidential Fusion Method of Feature Points Matching for the Visual Odometry

摘要

As the front end of visual simultaneous localization and mapping (vSLAM), the feature points matching of the visual odometry (VO) provides important robot pose judgment, while there are mismatches in the feature points matching due to inaccurate information obtained from cameras. In the process of feature points matching, VO initializes the matching of feature points through two adjacent frames of pictures, as SLAM runs, the cumulative error will gradually increase, resulting in mapping and positioning results inaccurate. Aiming to reduce the feature points matching error during initialization, an evidential fusion method for feature points matching is put forward. On the basis of descriptor similarity in Oriented FAST and Rotated BRIEF SLAM (ORB-SLAM) and pixel similarity around feature points, we treat the similarity index as an evidence, and take Belief Jensen-Shannon (BJS) divergence to measure distance between different evidences. The proposed evidential method considers the uncertainties brought by different indexes in accord with the feature points matching, and obtains suitable matching points reducing false matches through the combination of evidences. BJS divergence measure is carried out to measure the discrepancy and conflict degree among the evidences. After that, the reliability of evidence could be obtained to modify the information volume of the evidences. Finally, an example of feature points matching shows that the evidential method is feasible for feature points matching.