无标记机器人检测与多智能体SLAM的6D位姿估计
Markerless Robot Detection and 6D Pose Estimation for Multi-Agent SLAM
AI总结：
本文提出基于深度学习的无标记机器人6D位姿估计方法，用于提升多智能体SLAM系统的相对定位精度。
摘要：
多机器人SLAM方法将不同观察者的定位历史和地图融合的能力常常受到建立数据关联难度的挑战。在感知混叠的上下文中，或当视角差异显著时，不同机器人感知输入之间的环闭检测容易受损。因此，机器人之间的直接相互观察是连接部分SLAM图的强大方式，但通常依赖于校准的标记数组（例如AprilTag数组）的存在，这严重限制了观察范围，并且在强光条件下（如反射或过曝）经常失效。在本文中，我们提出了一种新的解决方案，利用近期深度学习基于的6D位姿估计的最新进展。我们将无标记位姿估计作为分布式多机器人SLAM系统的一部分，并展示了其对机器人团队间相对定位精度的益处。该解决方案在行星类环境测试场录制的数据上进行了实验验证。
英文摘要：
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.
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。
