LIC-Fusion：LiDAR・慣性・カメラ融合オドメトリ
LIC-Fusion: LiDAR-Inertial-Camera Odometry
IMU・疎な視覚特徴・LiDAR点を密結合し、3センサ間のオンライン時空間キャリブレーションを行うMSCKFベースの融合オドメトリを提案。屋内屋外実験でVIOやLiDARオドメトリより高精度・高ロバスト。
著者: Xingxing Zuo, Patrick Geneva, Woosik Lee, Yong Liu, Guoquan Huang
分類: cs.RO
原文アブストラクト
This paper presents a tightly-coupled multi-sensor fusion algorithm termed LiDAR-inertial-camera fusion (LIC-Fusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points. In particular, the proposed LIC-Fusion performs online spatial and temporal sensor calibration between all three asynchronous sensors, in order to compensate for possible calibration variations. The key contribution is the optimal (up to linearization errors) multi-modal sensor fusion of detected and tracked sparse edge/surf feature points from LiDAR scans within an efficient MSCKF-based framework, alongside sparse visual feature observations and IMU readings. We perform extensive experiments in both indoor and outdoor environments, showing that the proposed LIC-Fusion outperforms the state-of-the-art visual-inertial odometry (VIO) and LiDAR odometry methods in terms of estimation accuracy and robustness to aggressive motions.
