SKiD-SLAM：资源受限野外环境下鲁棒、轻量的分布式多机器人激光雷达SLAM
SKiD-SLAM: Robust, Lightweight, and Distributed Multi-Robot LiDAR SLAM in Resource-Constrained Field Environments
AI总结：
针对野外环境下分布式激光雷达SLAM的资源受限与关联误差问题，提出轻量鲁棒的SKiD-SLAM多机器人建图框架，经多场景实验验证性能优于现有方法，具备野外应用潜力。
摘要：
分布式激光雷达SLAM对实现高效机器人自主能力、提升建图可扩展性至关重要。然而将其应用于野外环境时需考虑两个问题：一是资源受限，二是机器人间/机器人内的数据关联。资源受限问题源于数据规模超出网络或内存的处理能力，尤其是在野外使用通信系统或机载计算机时更为突出。机器人间/机器人内的关联问题则是因为大视角差异下ICP（迭代最近点）的收敛区域狭窄，会触发大量误阳性回环，最终导致多机器人系统的全局地图不一致。为解决这些问题，我们提出了一种面向通用野外应用的分布式激光雷达SLAM框架，名为SKiD-SLAM。在我们此前仅聚焦轻量地点识别与快速鲁棒全局配准的研究基础上，我们提出了一种多机器人建图框架，重点解决分布式激光雷达SLAM中鲁棒且轻量的机器人间回环检测问题。通过多组环境实验我们证明，与其他最先进的分布式SLAM方法相比，我们的方法更具鲁棒性且更轻量，能够克服资源受限与机器人间/机器人内关联问题。此外，我们通过在自研的真实行星模拟地形与洞穴环境数据集上开展建图实验，验证了该方法的野外适用性。我们的代码将发布于https://sparolab.github.io/research/skid_slam/。
英文摘要：
Distributed LiDAR SLAM is crucial for achieving efficient robot autonomy and improving the scalability of mapping. However, two issues need to be considered when applying it in field environments: one is resource limitation, and the other is inter/intra-robot association. The resource limitation issue arises when the data size exceeds the processing capacity of the network or memory, especially when utilizing communication systems or onboard computers in the field. The inter/intra-robot association issue occurs due to the narrow convergence region of ICP under large viewpoint differences, triggering many false positive loops and ultimately resulting in an inconsistent global map for multi-robot systems. To tackle these problems, we propose a distributed LiDAR SLAM framework designed for versatile field applications, called SKiD-SLAM. Extending our previous work that solely focused on lightweight place recognition and fast and robust global registration, we present a multi-robot mapping framework that focuses on robust and lightweight inter-robot loop closure in distributed LiDAR SLAM. Through various environmental experiments, we demonstrate that our method is more robust and lightweight compared to other state-of-the-art distributed SLAM approaches, overcoming resource limitation and inter/intra-robot association issues. Also, we validated the field applicability of our approach through mapping experiments in real-world planetary emulation terrain and cave environments, which are in-house datasets. Our code will be available at https://sparolab.github.io/research/skid_slam/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
