用于扫描匹配辅助定位的不变扩展卡尔曼滤波设计
Invariant EKF Design for Scan Matching-aided Localization
AI总结：
本文提出基于IEKF和MEKF的室内定位方法，通过融合运动传感器数据与扫描匹配点云实现机器人位姿估计，实验验证了IEKF的优越性。
摘要：
室内定位技术通过融合车载运动传感器数据与环境读数来估计机器人的位姿，本文在低成本Kinect深度相机捕获的扫描匹配点云情况下，提出基于不变扩展卡尔曼滤波（IEKF）和乘法扩展卡尔曼滤波（MEKF）的解决方案。两种设计在实验中成功验证，证明了IEKF设计的优势。
英文摘要：
Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design.
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。
