用于2D视觉惯性里程计SLAM的分布式块坐标移动 horizon 估计
Distributed Block Coordinate Moving Horizon Estimation for 2D Visual-Inertial-Odometry SLAM
AI总结：
本文提出一种基于分布式块坐标非线性Moving Horizon Estimation的VIO-SLAM算法，利用可并行化的Bundle Adjustment技术更新landmarks位置，仿真表明圆形和摆动轨迹的估计性能优于直线轨迹。
摘要：
本文提出了一种基于分布式块坐标非线性Moving Horizon Estimation方案的Visual Inertial Odometry Landmark-based Simultaneous Localisation and Mapping算法。该方法的主要优点是，对landmarks位置的更新基于一种可在landmarks上并行化的Bundle Adjustment技术。通过在不同环境和不同类型机器人轨迹的仿真中证明了该方法的性能。轨迹中的圆形和摆动模式比直线模式具有更好的估计性能，这证实了近期非线性可观测性理论的预期。
英文摘要：
This paper presents a Visual Inertial Odometry Landmark-based Simultaneous Localisation and Mapping algorithm based on a distributed block coordinate nonlinear Moving Horizon Estimation scheme. The main advantage of the proposed method is that the updates on the position of the landmarks are based on a Bundle Adjustment technique that can be parallelised over the landmarks. The performance of the method is demonstrated in simulations in different environments and with different types of robot trajectory. Circular and wiggling patterns in the trajectory lead to better estimation performance than straight ones, confirming what is expected from recent nonlinear observability theory.
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。
