Globally-Optimal Inlier Maximization for Relative Pose Estimation Under Planar Motion - Liu, Haotian; Chen, Guang; Liu, Yinlong; Liang, Zichen; Zhang, Ruiqi; Knoll, Alois (Frontiers in Neurorobotics, 2022)
Planar motion constraint occurs in VO (visual odometry) and SLAM for AGVs (Automated Guided Vehicle) or mobile robots in general. Conventionally, two-point solvers can be nested to RANdom SAmple Consensus to reject outliers in real data, but the performance descends when the ratio of outliers goes high. This paper proposes a globally-optimal BnB (branch-and-bound) solver for relative pose estimation under general planar motion, which aims to figure out the globally-optimal solution even under a quite noisy environment. Through reasonable modification of the motion equation, we decouple the relative pose into relative rotation and translation so that a simplified bounding strategy can be applied. It enhances the efficiency of BnB technique. Experimental results support the global optimality and demonstrate that the proposed method performs more robustly than existing approaches. In addition, the proposed algorithm outperforms state-of-art methods in global optimality under varying level of outliers.