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.

翻译：视觉重定位对于移动机器人的自主视觉定位与导航至关重要。得益于基于CNN的目标检测算法的进步，视觉重定位的鲁棒性显著提升，尤其是在传统方法失效的视角场景中。然而，由轴对齐目标检测生成的椭球体（二次曲面）可能限制目标级表示的精度，并降低视觉重定位系统的性能。本文提出一种自动目标级体素建模的新方法，以实现对象椭球体的精确表示。针对视觉重定位问题，我们设计了一种更优的相机姿态恢复优化策略，以充分利用二维拟合椭圆与三维精确椭球体的投影特性。上述所有模块均被完整集成至视觉SLAM系统中。实验结果表明，本文提出的语义级对象建图方法与基于目标的视觉重定位方法在提升新视角鲁棒性方面显著增强了视觉重定位的性能。