動的環境向け高速SLAM：モデル不要の物体認識型アプローチ
Dynamic SLAM: The Need For Speed
セマンティックセグメンテーションを活用し、物体の3Dモデルや姿勢推定を必要とせずに剛体物体の速度をリアルタイム推定する、モデルフリーな物体認識型動的SLAMアルゴリズムを提案した。
著者: Mina Henein, Jun Zhang, Robert Mahony, Viorela Ila
分類: cs.RO
原文アブストラクト
The static world assumption is standard in most simultaneous localisation and mapping (SLAM) algorithms. Increased deployment of autonomous systems to unstructured dynamic environments is driving a need to identify moving objects and estimate their velocity in real-time. Most existing SLAM based approaches rely on a database of 3D models of objects or impose significant motion constraints. In this paper, we propose a new feature-based, model-free, object-aware dynamic SLAM algorithm that exploits semantic segmentation to allow estimation of motion of rigid objects in a scene without the need to estimate the object poses or have any prior knowledge of their 3D models. The algorithm generates a map of dynamic and static structure and has the ability to extract velocities of rigid moving objects in the scene. Its performance is demonstrated on simulated, synthetic and real-world datasets.
