VIRAL-Fusion：視覚・慣性・測距・LiDARの統合センサフュージョン手法
VIRAL-Fusion: A Visual-Inertial-Ranging-Lidar Sensor Fusion Approach
UAVの15次元状態を推定するため、IMU・UWB測距・視覚慣性オドメトリ・LiDARオドメトリをスライディングウィンドウ最適化で融合する手法を提案し、公開データセットやシミュレータ、実飛行で評価した論文。
著者: Thien-Minh Nguyen, Shenghai Yuan, Muqing Cao, Yang Lyu, Thien Hoang Nguyen, Lihua Xie
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
In recent years, Onboard Self Localization (OSL) methods based on cameras or Lidar have achieved many significant progresses. However, some issues such as estimation drift and feature-dependence still remain inherent limitations. On the other hand, infrastructure-based methods can generally overcome these issues, but at the expense of some installation cost. This poses an interesting problem of how to effectively combine these methods, so as to achieve localization with long-term consistency as well as flexibility compared to any single method. To this end, we propose a comprehensive optimization-based estimator for 15-dimensional state of an Unmanned Aerial Vehicle (UAV), fusing data from an extensive set of sensors: inertial measurement units (IMUs), Ultra-Wideband (UWB) ranging sensors, and multiple onboard Visual-Inertial and Lidar odometry subsystems. In essence, a sliding window is used to formulate a sequence of robot poses, where relative rotational and translational constraints between these poses are observed in the IMU preintegration and OSL observations, while orientation and position are coupled in body-offset UWB range observations. An optimization-based approach is developed to estimate the trajectory of the robot in this sliding window. We evaluate the performance of the proposed scheme in multiple scenarios, including experiments on public datasets, high-fidelity graphical-physical simulator, and field-collected data from UAV flight tests. The result demonstrates that our integrated localization method can effectively resolve the drift issue, while incurring minimal installation requirements.
