SO-SLAM：スケール比例・対称テクスチャ制約を用いた意味オブジェクトSLAM
SO-SLAM: Semantic Object SLAM with Scale Proportional and Symmetrical Texture Constraints
物体のスケール比例・対称テクスチャ・平面支持という空間制約を導入し、単眼カメラでの物体初期化と姿勢最適化を頑健化した物体SLAMシステムを提案した。
著者: Ziwei Liao, Yutong Hu, Jiadong Zhang, Xianyu Qi, Xiaoyu Zhang, Wei Wang
分類: cs.RO, cs.AI
原文アブストラクト
Object SLAM introduces the concept of objects into Simultaneous Localization and Mapping (SLAM) and helps understand indoor scenes for mobile robots and object-level interactive applications. The state-of-art object SLAM systems face challenges such as partial observations, occlusions, unobservable problems, limiting the mapping accuracy and robustness. This paper proposes a novel monocular Semantic Object SLAM (SO-SLAM) system that addresses the introduction of object spatial constraints. We explore three representative spatial constraints, including scale proportional constraint, symmetrical texture constraint and plane supporting constraint. Based on these semantic constraints, we propose two new methods - a more robust object initialization method and an orientation fine optimization method. We have verified the performance of the algorithm on the public datasets and an author-recorded mobile robot dataset and achieved a significant improvement on mapping effects. We will release the code here: https://github.com/XunshanMan/SoSLAM.
