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：全自主空中集群的关键技术之一是实现空中机器人相对位姿估计与全局一致轨迹的协同SLAM（CSLAM）。然而现有CSLAM系统并未优先优化相对定位精度，而这对于无人机间的紧密协作至关重要。本文提出$D^2$SLAM——一种新颖的去中心化分布式（$D^2$）CSLAM系统，涵盖两种场景：近距离高精度状态估计的近场估计与全局一致轨迹估计的远场估计。$D^2$SLAM配备通用且强大的前端，支持立体相机或全景相机作为输入，前者易于获取，后者则是解决相对定位中视场角问题的优异方案。实验验证了$D^2$SLAM在自运动估计、相对定位及全局一致性方面均实现了高精度。此外，系统采用分布式优化算法实现$D^2$目标，支持集群规模扩展并保证对网络延迟的鲁棒性。我们论证$D^2$SLAM可广泛应用于实际场景。