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.

翻译：高效、准确且灵活的机器人相对定位在空地协同任务中至关重要。然而，当前机器人相对定位方法主要采用具有相同传感器配置的分布式多机器人SLAM系统实现，这些系统与所有机器人的状态估计紧密耦合，限制了灵活性与精度。为此，我们充分利用无人地面车辆（UGV）的高负载能力集成多种传感器，构建了一种半分布式跨模态空地相对定位框架。在本工作中，UGV与无人飞行器（UAV）均独立执行SLAM，同时提取基于深度学习的关键点与全局描述符，从而将相对定位从所有智能体的状态估计中解耦。UGV通过融合激光雷达、相机与惯性测量单元（IMU）进行局部光束法平差（BA），快速获取精确的相对位姿估计。该BA过程采用稀疏关键点优化，分为两个阶段：首先优化从激光雷达-惯性里程计（LIO）插值得到的相机位姿，随后估计UGV与UAV之间的相对相机位姿。此外，我们实现了基于深度学习描述符的增量式回环检测算法，以高效维护与检索关键帧。实验结果表明，本方法在精度与效率方面均表现出色。与传统多机器人SLAM方法传输图像或点云不同，本方法仅传输关键点像素及其描述符，将通信带宽有效限制在0.3 Mbps以下。代码与数据将公开于https://github.com/Ascbpiac/cross-model-relative-localization.git。