带闭环检测的在线一维磁场SLAM
Online One-Dimensional Magnetic Field SLAM with Loop-Closure Detection
AI总结：
本文提出一种轻量级一维磁场SLAM方法，将磁场读数作为一维轨迹进行闭环检测，并结合扩展卡尔曼平滑器融合里程计增量与闭环时刻，用于校正里程计漂移，通过手持iPad室内实验验证了其实用性。
摘要：
我们提出一种轻量级磁场同时定位与建图（SLAM）方法，用于里程计路径中的漂移校正，其关注点纯粹在于里程计而非地图构建。我们将过去的磁场读数表示为一维轨迹，并将当前磁场观测与该轨迹进行匹配。该方法归结为基于当前位姿状态估计和磁场的顺序闭环检测与决策。我们将该设置与使用扩展卡尔曼平滑器的路径估计框架相结合，该平滑器将里程计增量与检测到的闭环时刻进行融合。我们通过在室内场景中移动的手持iPad的若干不同真实世界示例，展示了该模型的实用适用性。
英文摘要：
We present a lightweight magnetic field simultaneous localisation and mapping (SLAM) approach for drift correction in odometry paths, where the interest is purely in the odometry and not in map building. We represent the past magnetic field readings as a one-dimensional trajectory against which the current magnetic field observations are matched. This approach boils down to sequential loop-closure detection and decision-making, based on the current pose state estimate and the magnetic field. We combine this setup with a path estimation framework using an extended Kalman smoother which fuses the odometry increments with the detected loop-closure timings. We demonstrate the practical applicability of the model with several different real-world examples from a handheld iPad moving in indoor scenes.
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。
