Collaborative simultaneous localization and mapping (CSLAM) is essential for autonomous aerial swarms, laying the foundation for downstream algorithms such as planning and control. To address existing CSLAM systems' limitations in relative localization accuracy, crucial for close-range UAV collaboration, this paper introduces $D^2$SLAM-a novel decentralized and distributed CSLAM system. $D^2$SLAM innovatively manages near-field estimation for precise relative state estimation in proximity and far-field estimation for consistent global trajectories. Its adaptable front-end supports both stereo and omnidirectional cameras, catering to various operational needs and overcoming field-of-view challenges in aerial swarms. Experiments demonstrate $D^2$SLAM's effectiveness in accurate ego-motion estimation, relative localization, and global consistency. Enhanced by distributed optimization algorithms, $D^2$SLAM exhibits remarkable scalability and resilience to network delays, making it well-suited for a wide range of real-world aerial swarm applications. The adaptability and proven performance of $D^2$SLAM represent a significant advancement in autonomous aerial swarm technology.

翻译：协同同步定位与建图（CSLAM）是自主空中集群的关键技术，为规划与控制等下游算法奠定基础。针对现有CSLAM系统在相对定位精度（对无人机近距离协同至关重要）方面的局限，本文提出D²SLAM——一种新颖的分散式与分布式CSLAM系统。D²SLAM创新性地通过近场估计实现近距离精确相对状态估计，通过远场估计保持全局轨迹一致性。其自适应前端同时支持立体相机与全向相机，满足多样化任务需求并克服空中集群的视野局限。实验证明D²SLAM在自身运动估计、相对定位与全局一致性方面具有卓越性能。通过分布式优化算法增强，D²SLAM展现出优异的可扩展性及对网络延迟的强鲁棒性，适用于广泛的现实世界空中集群应用场景。D²SLAM的适应性与已验证的性能，标志着自主空中集群技术的重大进展。