単眼カメラのための証明可能な最適ハンドアイキャリブレーション
Certifiably Optimal Monocular Hand-Eye Calibration
スケール未知の単眼カメラを含むハンドアイ外部キャリブレーションを、測定ノイズが有界な条件下で大域最適かつ証明可能に解く凸最適化手法を提案し、合成データ実験で最適性と速度を示した。
著者: Emmett Wise, Matthew Giamou, Soroush Khoubyarian, Abhinav Grover, Jonathan Kelly
分類: cs.RO
原文アブストラクト
Correct fusion of data from two sensors is not possible without an accurate estimate of their relative pose, which can be determined through the process of extrinsic calibration. When two or more sensors are capable of producing their own egomotion estimates (i.e., measurements of their trajectories through an environment), the 'hand-eye' formulation of extrinsic calibration can be employed. In this paper, we extend our recent work on a convex optimization approach for hand-eye calibration to the case where one of the sensors cannot observe the scale of its translational motion (e.g., a monocular camera observing an unmapped environment). We prove that our technique is able to provide a certifiably globally optimal solution to both the known- and unknown-scale variants of hand-eye calibration, provided that the measurement noise is bounded. Herein, we focus on the theoretical aspects of the problem, show the tightness and stability of our solution, and demonstrate the optimality and speed of our algorithm through experiments with synthetic data.
