マルチカメラ構成のためのグラフベースハンドアイキャリブレーション最適化フレームワーク
A Graph-based Optimization Framework for Hand-Eye Calibration for Multi-Camera Setups
ハンドアイキャリブレーションをSLAMのポーズグラフ問題として捉え、単一カメラのeye-on-base構成とマルチカメラ構成に拡張したグラフベース最適化手法を提案し、シミュレーションと実機で従来手法を上回る精度を示した。
著者: Daniele Evangelista, Emilio Olivastri, Davide Allegro, Emanuele Menegatti, Alberto Pretto
分類: cs.RO
原文アブストラクト
Hand-eye calibration is the problem of estimating the spatial transformation between a reference frame, usually the base of a robot arm or its gripper, and the reference frame of one or multiple cameras. Generally, this calibration is solved as a non-linear optimization problem, what instead is rarely done is to exploit the underlying graph structure of the problem itself. Actually, the problem of hand-eye calibration can be seen as an instance of the Simultaneous Localization and Mapping (SLAM) problem. Inspired by this fact, in this work we present a pose-graph approach to the hand-eye calibration problem that extends a recent state-of-the-art solution in two different ways: i) by formulating the solution to eye-on-base setups with one camera; ii) by covering multi-camera robotic setups. The proposed approach has been validated in simulation against standard hand-eye calibration methods. Moreover, a real application is shown. In both scenarios, the proposed approach overcomes all alternative methods. We release with this paper an open-source implementation of our graph-based optimization framework for multi-camera setups.
