Visual odometry algorithm based on geometric prior for dynamic environments

Abstract

AbstractSimultaneous localization and mapping (SLAM) is considered to be an important way for some smart devices to perform automatic path planning, and many successful SLAM systems have been developed in the past few years. Most existing approaches rely heavily on static world assumptions, and such strong assumptions limit the application of most vSLAM (visual SLAM) in complex dynamic reality environments, where dynamic objects often lead to incorrect data association in tracking, which reduces the overall accuracy and robustness of the system and causes tracking crashes. The dynamic objects in the map may change over time; thus, distinguishing dynamic information in a scene is challenging. In order to solve the interference problem of dynamic objects, most point-based visual odometry algorithms have concentrated on feature matching or direct pixel intensity matching, disregarding an ordinary but crucial image entity: geometric information. In this article, we put forward a novel visual odometry algorithm based on dynamic point detection methods called geometric prior and constraints. It removes the moving objects by combining the spatial geometric information of the image and depends on the remaining features to estimate the position of the camera. To the best of our knowledge, our proposed algorithm achieves superior performance over existing methods on a variety of public datasets.