PGD-VIO: 平面支援とグラフベースドリフト抑制による高精度な視覚慣性オドメトリ
PGD-VIO: An Accurate Plane-Aided Visual-Inertial Odometry with Graph-Based Drift Suppression
RGBDカメラとIMUを用い、点と平面の特徴をEKFで統合し、平面マップの構造をグラフで照合して累積ドリフトを抑える視覚慣性オドメトリを提案した。
著者: Yidi Zhang, Fulin Tang, Zewen Xu, Yihong Wu, Pengju Ma
分類: cs.RO
原文アブストラクト
Generally, high-level features provide more geometrical information compared to point features, which can be exploited to further constrain motions. Planes are commonplace in man-made environments, offering an active means to reduce drift, due to their extensive spatial and temporal observability. To make full use of planar information, we propose a novel visual-inertial odometry (VIO) using an RGBD camera and an inertial measurement unit (IMU), effectively integrating point and plane features in an extended Kalman filter (EKF) framework. Depth information of point features is leveraged to improve the accuracy of point triangulation, while plane features serve as direct observations added into the state vector. Notably, to benefit long-term navigation,a novel graph-based drift detection strategy is proposed to search overlapping and identical structures in the plane map so that the cumulative drift is suppressed subsequently. The experimental results on two public datasets demonstrate that our system outperforms state-of-the-art methods in localization accuracy and meanwhile generates a compact and consistent plane map, free of expensive global bundle adjustment and loop closing techniques.
