The capability of multi-robot SLAM approaches to merge localization history and maps from different observers is often challenged by the difficulty in establishing data association. Loop closure detection between perceptual inputs of different robotic agents is easily compromised in the context of perceptual aliasing, or when perspectives differ significantly. For this reason, direct mutual observation among robots is a powerful way to connect partial SLAM graphs, but often relies on the presence of calibrated arrays of fiducial markers (e.g., AprilTag arrays), which severely limits the range of observations and frequently fails under sharp lighting conditions, e.g., reflections or overexposure. In this work, we propose a novel solution to this problem leveraging recent advances in Deep-Learning-based 6D pose estimation. We feature markerless pose estimation as part of a decentralized multi-robot SLAM system and demonstrate the benefit to the relative localization accuracy among the robotic team. The solution is validated experimentally on data recorded in a test field campaign on a planetary analogous environment.

翻译：多机器人SLAM方法在融合不同观测者的定位历史与地图时，常因数据关联的困难而面临挑战。当存在感知混叠或视角差异显著时，不同机器人智能体之间的感知输入闭环检测极易失效。因此，机器人间的直接相互观测成为连接局部SLAM图的有效途径，但该方法通常依赖于已标定的基准标记阵列（例如AprilTag阵列），这严重限制了观测范围，且在强反射或过曝等极端光照条件下经常失效。本研究基于深度学习六自由度位姿估计的最新进展，提出了一种解决该问题的新方案。我们将无标记位姿估计作为去中心化多机器人SLAM系统的组成部分，并论证了其对机器人团队间相对定位精度的提升作用。该方案通过行星模拟环境测试场采集的数据进行了实验验证。