A Monocular Visual Localization Algorithm for Large-Scale Indoor Environments through Matching a Prior Semantic Map

Abstract

It is challenging for a visual SLAM system to keep long-term precise and robust localization ability in a large-scale indoor environment since there is a low probability of the occurrence of loop closure. Aiming to solve this problem, we propose a monocular visual localization algorithm for large-scale indoor environments through matching a prior semantic map. In the approach, the line features of certain semantic objects observed by the monocular camera are extracted in real time. A cost function is proposed to represent the difference between the observed objects and the matched semantic objects in the preexisting semantic map. After that, a bundle adjustment model integrating the semantic object matching difference is given to optimize the pose of the camera and the real-time environment map. Finally, test cases are designed to evaluate the performance of our approach, in which the line features with semantic information are extracted in advance to build the semantic map for matching in real time. The test results show that the positioning accuracy of our method is improved in large-scale indoor navigation.