PG-LIO: 光度・幾何情報融合による堅牢なLiDAR慣性オドメトリ
PG-LIO: Photometric-Geometric fusion for Robust LiDAR-Inertial Odometry
LiDARの光度情報と幾何情報をIMU制約とともに因子グラフで融合し、幾何構造が乏しい自己相似環境でも高精度に動作するリアルタイムLiDAR慣性オドメトリを提案した。
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著者: Nikhil Khedekar, Kostas Alexis
分類: cs.RO
原文アブストラクト
LiDAR-Inertial Odometry (LIO) is widely used for accurate state estimation and mapping which is an essential requirement for autonomous robots. Conventional LIO methods typically rely on formulating constraints from the geometric structure sampled by the LiDAR. Hence, in the lack of geometric structure, these tend to become ill-conditioned (degenerate) and fail. Robustness of LIO to such conditions is a necessity for its broader deployment. To address this, we propose PG-LIO, a real-time LIO method that fuses photometric and geometric information sampled by the LiDAR along with inertial constraints from an Inertial Measurement Unit (IMU). This multi-modal information is integrated into a factor graph optimized over a sliding window for real-time operation. We evaluate PG-LIO on multiple datasets that include both geometrically well-conditioned as well as self-similar scenarios. Our method achieves accuracy on par with state-of-the-art LIO in geometrically well-structured settings while significantly improving accuracy in degenerate cases including against methods that also fuse intensity. Notably, we demonstrate only 1 m drift over a 1 km manually piloted aerial trajectory through a geometrically self-similar tunnel at an average speed of 7.5m/s (max speed 10.8 m/s). For the benefit of the community, we shall also release our source code https://github.com/ntnu-arl/mimosa
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