论文标题
部分可观测时空混沌系统的无模型预测
$D^2$SLAM: Decentralized and Distributed Collaborative Visual-inertial SLAM System for Aerial Swarm
论文作者
论文摘要
协作同时定位和映射（CSLAM）对于自主空中群是必不可少的，为下游算法（例如计划和控制）奠定了基础。为了解决现有的CSLAM系统在相对本地化准确性方面的限制，对于近距离无人机协作至关重要，本文介绍了$ d^2 $ slam-a小说分散和分布式CSLAM系统。 $ d^2 $创新地管理近场估计，以确切的相对状态估计在近端和远场估算一致的全球轨迹上。它的适应性前端支持立体声和全向相机，以满足各种操作需求，并克服空中群中的视野挑战。实验证明了$ d^2 $ SLAM在准确的自我估计，相对定位和全球一致性方面的有效性。通过分布式优化算法增强，$ d^2 $ SLAM对网络延迟具有显着的可扩展性和弹性，使其非常适合各种现实世界中的空中群应用。 $ d^2 $ slam的适应性和可靠的性能代表了自动空中群技术的重大进步。
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.
