A crucial technology in fully autonomous aerial swarms is collaborative SLAM (CSLAM), which enables the estimation of relative pose and global consistent trajectories of aerial robots. However, existing CSLAM systems do not prioritize relative localization accuracy, critical for close collaboration among UAVs. This paper presents $D^2$SLAM, a novel decentralized and distributed ($D^2$) CSLAM system that covers two scenarios: near-field estimation for high accuracy state estimation in close range and far-field estimation for consistent global trajectory estimation. $D^2$SLAM has a versatile and powerful front-end that can use stereo cameras or omnidirectional cameras as input, the former being easy to obtain and the latter being an excellent solution to the Field of View problem in relative localization. Our experiments verify $D^2$SLAM achieves high accuracy in ego-motion estimation, relative localization, and global consistency. Moreover, distributed optimization algorithms are adopted to achieve the $D^2$ objective to allow the scale-up of the swarm and ensure robustness against network delays. We argue $D^2$SLAM can be applied in a wide range of real-world applications.
翻译:在完全自主的空中群落中,一个至关重要的技术是合作性的SLAM(CSLAM),它能够估计航空机器人的相对面貌和全球一致的轨迹;然而,现有的CSLAM系统并不优先考虑相对本地化的准确性,这对无人驾驶飞行器之间的密切合作至关重要。本文展示的是$D2$SLAM,这是一个新的分散和分布式的(D2$)CSLAM系统,涵盖两种情景:近地点估算近距离高精确度估计,远地点估算一致的全球轨迹估计。 $D2$SLAM有一个多功能和强大的前端,可以使用立体照相机或全射线照相机作为输入,前者容易获得,后者是相对本地化问题的一个极好的解决办法。我们的实验核实$D2SLAM在自我提升估计、相对本地化和全球一致性方面达到很高的准确性。此外,还采用了分布式优化算法,以实现$D2$2美元的全球轨迹估计目标,以便能够扩大超宽度,并确保网络延迟应用。我们争论的是,在现实范围内可以应用$D2$SLAM。</s>