SemLoc: Accurate and Robust Visual Localization with Semantic and Structural Constraints from Prior Maps
Shiwen Liang, Yunzhou Zhang, Rui Tian, Delong Zhu, Linghao Yang, Zhenzhong Cao
Abstract
Semantic information and geometrical structures of a prior map can be leveraged in visual localization to bound drift errors and improve accuracy. In this paper, we propose SemLoc, a pure visual localization system, for accurate localization in a prior semantic map. To tightly couple semantic and structure information from prior maps, a hybrid constraint is presented by using the Dirichlet distribution. Then, with the local landmarks and their semantic states tracked in the frontend, the camera poses and data associations are jointly optimized through Expectation-Maximization (EM) algorithm. We validate the effectiveness of our approach in both monocular and stereo modes on the public KITTI dataset. Experimental results demonstrate that our system can greatly reduce drift errors with an satisfying real-time performance. Compared with several state-of-the-art visual localization systems, the proposed framework achieves a competitive localization performance.