Frontiers in Neurorobotics (Mar 2022)

Globally-Optimal Inlier Maximization for Relative Pose Estimation Under Planar Motion

  • Haotian Liu,
  • Haotian Liu,
  • Guang Chen,
  • Guang Chen,
  • Yinlong Liu,
  • Zichen Liang,
  • Zichen Liang,
  • Ruiqi Zhang,
  • Ruiqi Zhang,
  • Alois Knoll

DOI
https://doi.org/10.3389/fnbot.2022.820703
Journal volume & issue
Vol. 16

Abstract

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Planar motion constraint occurs in visual odometry (VO) and SLAM for Automated Guided Vehicles (AGVs) 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 study proposes a globally-optimal Branch-and-Bound (BnB) 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 the 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 the varying level of outliers.

Keywords