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 article 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. We believe the adaptability and proven performance of $D^{2}$ SLAM signify a notable advancement in autonomous aerial swarm technology.

中文翻译：

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$D^{2}$SLAM：用于空中集群的去中心化分布式协作视觉惯性 SLAM 系统


协作同步定位与建图 (CSLAM) 对于自主空中集群至关重要，为规划和控制等下游算法奠定了基础。为了解决现有 CSLAM 系统在相对定位精度方面的局限性（这对于近距离无人机协作至关重要），本文介绍了 $D^{2}$ SLAM——一种新颖的去中心化分布式 CSLAM 系统。 $D^{2}$ SLAM 创新性地管理近场估计，以实现邻近区域的精确相对状态估计，并管理远场估计，以实现一致的全局轨迹。其适应性强的前端支持立体和全向摄像机，可满足各种操作需求并克服空中集群中的视野挑战。实验证明了 $D^{2}$ SLAM 在准确的自我运动估计、相对定位和全局一致性方面的有效性。通过分布式优化算法的增强，$D^{2}$ SLAM 表现出卓越的可扩展性和对网络延迟的恢复能力，使其非常适合各种现实世界的空中集群应用。我们相信 $D^{2}$ SLAM 的适应性和经过验证的性能标志着自主空中集群技术的显着进步。