地図支援EKFによる高精度な単眼視覚慣性SLAM
Accurate Monocular Visual-inertial SLAM using a Map-assisted EKF Approach
単眼カメラと慣性センサを密結合し、EKF・バンドル調整・ループ閉じ込みを組み合わせて、CPU上でリアルタイムに高精度かつロバストな自己位置推定と地図生成を実現した。
著者: Meixiang Quan, Songhao Piao, Minglang Tan, Shi-Sheng Huang
分類: cs.RO
原文アブストラクト
This paper presents a novel tightly-coupled monocular visual-inertial Simultaneous Localization and Mapping algorithm, which provides accurate and robust localization within the globally consistent map in real time on a standard CPU. This is achieved by firstly performing the visual-inertial extended kalman filter(EKF) to provide motion estimate at a high rate. However the filter becomes inconsistent due to the well known linearization issues. So we perform a keyframe-based visual-inertial bundle adjustment to improve the consistency and accuracy of the system. In addition, a loop closure detection and correction module is also added to eliminate the accumulated drift when revisiting an area. Finally, the optimized motion estimates and map are fed back to the EKF-based visual-inertial odometry module, thus the inconsistency and estimation error of the EKF estimator are reduced. In this way, the system can continuously provide reliable motion estimates for the long-term operation. The performance of the algorithm is validated on public datasets and real-world experiments, which proves the superiority of the proposed algorithm.
