Semantic Object-level Modeling for Robust Visual Camera Relocalization
Yifan Zhu Lingjuan Miao Haitao Wu Zhiqiang Zhou Weiyi Chen Longwen Wu
Visual relocalization is crucial for autonomous visual localization and
navigation of mobile robotics. Due to the improvement of CNN-based object
detection algorithm, the robustness of visual relocalization is greatly
enhanced especially in viewpoints where classical methods fail. However,
ellipsoids (quadrics) generated by axis-aligned object detection may limit the
accuracy of the object-level representation and degenerate the performance of
visual relocalization system. In this paper, we propose a novel method of
automatic object-level voxel modeling for accurate ellipsoidal representations
of objects. As for visual relocalization, we design a better pose optimization
strategy for camera pose recovery, to fully utilize the projection
characteristics of 2D fitted ellipses and the 3D accurate ellipsoids. All of
these modules are entirely intergrated into visual SLAM system. Experimental
results show that our semantic object-level mapping and object-based visual
relocalization methods significantly enhance the performance of visual
relocalization in terms of robustness to new viewpoints.