$D^2$SLAM：面向无人机集群的去中心化分布式协作视觉惯性SLAM系统
$D^2$SLAM: Decentralized and Distributed Collaborative Visual-inertial SLAM System for Aerial Swarm
AI总结：
针对现有协作SLAM相对定位精度不足的问题，提出去中心化分布式的$D^2$SLAM系统，通过远近场结合估计、多相机适配前端和分布式优化，实现高精度定位与强鲁棒性，推动无人机集群技术发展。
摘要：
协作同时定位与建图（CSLAM）是自主无人机集群的核心技术，为规划、控制等下游算法奠定基础。针对现有CSLAM系统在近距离无人机协作关键的相对定位精度方面存在的局限，本文提出$D^2$SLAM——一种全新的去中心化分布式CSLAM系统。$D^2$SLAM创新性地采用近场估计实现近距离下的精确相对状态估计，同时通过远场估计保证全局轨迹的一致性。其自适应前端支持双目相机和全向相机，可满足多种作业需求，克服无人机集群中的视场受限问题。实验表明，$D^2$SLAM在精确自运动估计、相对定位和全局一致性方面均表现出色。借助分布式优化算法的加持，$D^2$SLAM具备显著的可扩展性和网络延迟鲁棒性，非常适用于各类真实无人机集群应用场景。$D^2$SLAM的适配性与经实验验证的性能，标志着自主无人机集群技术的一项重要进展。
英文摘要：
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.
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。
