Continuous Point Cloud Stitch Based on Image Feature Matching Constraint and Score

Continuous point cloud stitching provides accurate and rapid environmental information for mapping, which plays an essential role in autonomous vehicles. Many methods related to autonomous vehicles are either inaccurate or time-consuming when they reconstruct large scenes. In this work, a feature matching score-based stitching method to reconstruct environments for autonomous vehicles is proposed. The purpose of the method is to address the problems of stitching quality and time consumption by using the feature matchcompleteness scores. The feature matching score is obtained by calculating the matching rate within an adjacent image pairs that produces a frame of point cloud. The score maintains the correct points, so fewer points require to be stitched. Then, the corresponding criterion is given to evaluate the performance of stitching candidate point clouds. Finally, the points are fit into planes to form a complete and comprehensive scene. The proposed stitching algorithm was verified in several experiments to demonstrate the efficiency and the accuracy of the overall system.