マルチカメラ視覚自己位置推定のための効率的な2D-3Dマッチング
Efficient 2D-3D Matching for Multi-Camera Visual Localization
複数カメラと慣性センサを用い、優先度付き特徴マッチングと並列処理で大規模環境での視覚的自己位置推定を高速かつ頑健に行う手法を提案した。
著者: Marcel Geppert, Peidong Liu, Zhaopeng Cui, Marc Pollefeys, Torsten Sattler
分類: cs.RO
原文アブストラクト
Visual localization, i.e., determining the position and orientation of a vehicle with respect to a map, is a key problem in autonomous driving. We present a multicamera visual inertial localization algorithm for large scale environments. To efficiently and effectively match features against a pre-built global 3D map, we propose a prioritized feature matching scheme for multi-camera systems. In contrast to existing works, designed for monocular cameras, we (1) tailor the prioritization function to the multi-camera setup and (2) run feature matching and pose estimation in parallel. This significantly accelerates the matching and pose estimation stages and allows us to dynamically adapt the matching efforts based on the surrounding environment. In addition, we show how pose priors can be integrated into the localization system to increase efficiency and robustness. Finally, we extend our algorithm by fusing the absolute pose estimates with motion estimates from a multi-camera visual inertial odometry pipeline (VIO). This results in a system that provides reliable and drift-less pose estimation. Extensive experiments show that our localization runs fast and robust under varying conditions, and that our extended algorithm enables reliable real-time pose estimation.
