どの角度から見ても：LiDARによる意味的クロスビュー自己位置推定と地図構築
Any Way You Look At It: Semantic Crossview Localization and Mapping with LiDAR
自己中心的な3D意味ラベル付きLiDARとIMU、および衛星・航空機からの俯瞰RGB画像のみを用いて、ロボットをリアルタイムでグローバルに自己位置推定し、同時に意味的なグローバル地図を構築する手法を提案した論文。
著者: Ian D. Miller, Anthony Cowley, Ravi Konkimalla, Shreyas S. Shivakumar, Ty Nguyen, Trey Smith, Camillo Jose Taylor, Vijay Kumar
分類: cs.RO
原文アブストラクト
Currently, GPS is by far the most popular global localization method. However, it is not always reliable or accurate in all environments. SLAM methods enable local state estimation but provide no means of registering the local map to a global one, which can be important for inter-robot collaboration or human interaction. In this work, we present a real-time method for utilizing semantics to globally localize a robot using only egocentric 3D semantically labelled LiDAR and IMU as well as top-down RGB images obtained from satellites or aerial robots. Additionally, as it runs, our method builds a globally registered, semantic map of the environment. We validate our method on KITTI as well as our own challenging datasets, and show better than 10 meter accuracy, a high degree of robustness, and the ability to estimate the scale of a top-down map on the fly if it is initially unknown.
