半分散型クロスモーダル空中-地上相対位置推定
Semi-distributed Cross-modal Air-Ground Relative Localization
地上車両とドローンのSLAMを独立させ、LiDAR・カメラ・IMUを統合した局所バンドル調整により、高精度かつ効率的な相対位置推定を実現した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Weining Lu, Deer Bin, Lian Ma, Ming Ma, Zhihao Ma, Xiangyang Chen, Longfei Wang, Yixiao Feng, Zhouxian Jiang, Yongliang Shi, Bin Liang
分類: cs.RO, cs.CV
原文アブストラクト
Efficient, accurate, and flexible relative localization is crucial in air-ground collaborative tasks. However, current approaches for robot relative localization are primarily realized in the form of distributed multi-robot SLAM systems with the same sensor configuration, which are tightly coupled with the state estimation of all robots, limiting both flexibility and accuracy. To this end, we fully leverage the high capacity of Unmanned Ground Vehicle (UGV) to integrate multiple sensors, enabling a semi-distributed cross-modal air-ground relative localization framework. In this work, both the UGV and the Unmanned Aerial Vehicle (UAV) independently perform SLAM while extracting deep learning-based keypoints and global descriptors, which decouples the relative localization from the state estimation of all agents. The UGV employs a local Bundle Adjustment (BA) with LiDAR, camera, and an IMU to rapidly obtain accurate relative pose estimates. The BA process adopts sparse keypoint optimization and is divided into two stages: First, optimizing camera poses interpolated from LiDAR-Inertial Odometry (LIO), followed by estimating the relative camera poses between the UGV and UAV. Additionally, we implement an incremental loop closure detection algorithm using deep learning-based descriptors to maintain and retrieve keyframes efficiently. Experimental results demonstrate that our method achieves outstanding performance in both accuracy and efficiency. Unlike traditional multi-robot SLAM approaches that transmit images or point clouds, our method only transmits keypoint pixels and their descriptors, effectively constraining the communication bandwidth under 0.3 Mbps. Codes and data will be publicly available on https://github.com/Ascbpiac/cross-model-relative-localization.git.
