DAMS-LIO: 退化検知型モジュラーセンサフュージョンLiDAR慣性オドメトリ
DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry
LiDARと慣性センサに加え、退化を検知したときのみ他オドメトリの相対姿勢を統合する軽量iEKFベースのLiDAR慣性オドメトリを提案し、劣悪環境下でも高精度な状態推定を実現した。
著者: Fuzhang Han, Han Zheng, Wenjun Huang, Rong Xiong, Yue Wang, Yanmei Jiao
分類: cs.RO
原文アブストラクト
With robots being deployed in increasingly complex environments like underground mines and planetary surfaces, the multi-sensor fusion method has gained more and more attention which is a promising solution to state estimation in the such scene. The fusion scheme is a central component of these methods. In this paper, a light-weight iEKF-based LiDAR-inertial odometry system is presented, which utilizes a degeneration-aware and modular sensor-fusion pipeline that takes both LiDAR points and relative pose from another odometry as the measurement in the update process only when degeneration is detected. Both the Cramer-Rao Lower Bound (CRLB) theory and simulation test are used to demonstrate the higher accuracy of our method compared to methods using a single observation. Furthermore, the proposed system is evaluated in perceptually challenging datasets against various state-of-the-art sensor-fusion methods. The results show that the proposed system achieves real-time and high estimation accuracy performance despite the challenging environment and poor observations.
