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首页> 外文会议>Conference on unmanned systems technology >COMPARISON OF REAL-TIME PERFORMANCE OF KALMAN FILTER BASED SLAM METHODS FOR UNMANNED GROUND VEHICLE (UGV) NAVIGATION
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COMPARISON OF REAL-TIME PERFORMANCE OF KALMAN FILTER BASED SLAM METHODS FOR UNMANNED GROUND VEHICLE (UGV) NAVIGATION

机译：基于Kalman滤波器的实时性能比较无人机地面车辆（UGV）导航的基于SLAM方法

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Simultaneous Localization and Mapping (SLAM) using for the mobile robot navigation has two main problems. First problem is the computational complexity due to the growing state vector with the added landmark in the environment. Second problem is data association which matches the observations and landmarks in the state vector. In this study, we compare Extended Kalman Filter (EKF) based SLAM which is well-developed and well-known algorithm , and Compressed Extended Kalman Filter (CEKF) based SLAM developed for decreasing of the computational complexity of the EKF based SLAM. We write two simulation program to investigate these techniques. Firts program is written for the comparison of EKF and CEKF based SLAM according to the computational complexity and covariance matrix error with the different numbers of landmarks. In the second program, EKF and CEKF based SLAM simulations are presented. For this simulation differential drive vehicle that moves in a 10m square trajectory and LMS 200 2-D laser range finder are modelled and landmarks are randomly scattered in that 10m square environment.
机译：同时定位和映射（SLAM）使用移动机器人导航有两个主要问题。第一个问题是由于生长状态向量，在环境中添加了地标。第二个问题是数据关联，其与状态向量中的观察和地标匹配。在这项研究中，我们比较基于延长的卡尔曼滤波器（EKF）的SLAM，它是开发的良好且知名的算法，以及基于基于SLAM的压缩扩展卡尔曼滤波器（CEKF），用于降低基于EKF的SLAM的计算复杂性。我们编写了两个模拟程序来调查这些技术。根据具有不同数量的地标的计算复杂性和协方差矩阵错误，编写了FIRTS程序。在第二个程序中，提出了基于EKF和CEKF的SLAM模拟。对于在10M平方轨迹和LMS 200 2-D激光范围发现器中移动的这种模拟差动驱动器，建模和地标在该10M平方环境中随机分散。

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