単眼視覚慣性融合による動的物体のメートルスケール姿勢推定
Estimating Metric Poses of Dynamic Objects Using Monocular Visual-Inertial Fusion
単眼カメラとIMUを組み合わせ、動きの仮定なしに動的物体の軌跡を最適化してスケール復元し、6自由度姿勢を連続推定する手法を提案した。
著者: Kejie Qiu, Tong Qin, Hongwen Xie, Shaojie Shen
分類: cs.RO, cs.CV
原文アブストラクト
A monocular 3D object tracking system generally has only up-to-scale pose estimation results without any prior knowledge of the tracked object. In this paper, we propose a novel idea to recover the metric scale of an arbitrary dynamic object by optimizing the trajectory of the objects in the world frame, without motion assumptions. By introducing an additional constraint in the time domain, our monocular visual-inertial tracking system can obtain continuous six degree of freedom (6-DoF) pose estimation without scale ambiguity. Our method requires neither fixed multi-camera nor depth sensor settings for scale observability, instead, the IMU inside the monocular sensing suite provides scale information for both camera itself and the tracked object. We build the proposed system on top of our monocular visual-inertial system (VINS) to obtain accurate state estimation of the monocular camera in the world frame. The whole system consists of a 2D object tracker, an object region-based visual bundle adjustment (BA), VINS and a correlation analysis-based metric scale estimator. Experimental comparisons with ground truth demonstrate the tracking accuracy of our 3D tracking performance while a mobile augmented reality (AR) demo shows the feasibility of potential applications.
