屋内環境向けの二次曲面と対称性を利用した物体指向SLAM
Object-oriented SLAM using Quadrics and Symmetry Properties for Indoor Environments
RGB-Dカメラを用い、物体を二次曲面で表現する物体レベルSLAMを提案。単一フレームでの初期化と屋内人工物の対称性による遮蔽部推定で、平面軌道での観測性問題を改善し精度と収束速度を向上させた。
著者: Ziwei Liao, Wei Wang, Xianyu Qi, Xiaoyu Zhang, Lin Xue, Jianzhen Jiao, Ran Wei
分類: cs.RO, cs.CV
原文アブストラクト
Aiming at the application environment of indoor mobile robots, this paper proposes a sparse object-level SLAM algorithm based on an RGB-D camera. A quadric representation is used as a landmark to compactly model objects, including their position, orientation, and occupied space. The state-of-art quadric-based SLAM algorithm faces the observability problem caused by the limited perspective under the plane trajectory of the mobile robot. To solve the problem, the proposed algorithm fuses both object detection and point cloud data to estimate the quadric parameters. It finishes the quadric initialization based on a single frame of RGB-D data, which significantly reduces the requirements for perspective changes. As objects are often observed locally, the proposed algorithm uses the symmetrical properties of indoor artificial objects to estimate the occluded parts to obtain more accurate quadric parameters. Experiments have shown that compared with the state-of-art algorithm, especially on the forward trajectory of mobile robots, the proposed algorithm significantly improves the accuracy and convergence speed of quadric reconstruction. Finally, we made available an opensource implementation to replicate the experiments.
