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提出了一种基于信息势能鲁棒估计器来解决机器人室内的同时定位与地图构建(SLAM)问题.结构化的室内环境可以用线段近似表示.然而动态环境中,测距传感器测得的数据通常湮没在大量的噪声信号中.本文采用“分割与合并”(split-and-merge)方法进行线段的分类,根据信息势能的性能指标衡量每个采样数据对该线段的信息贡献量.按照信息优化理论设计估计器,选择信息量贡献大的样本点作为信息内点提取线段参数,构建局部地图.采用粒子滤波器进行地图及机器人路径的更新.采用递推的方法估计信息势能,降低了对样本点的信息量贡献做估计时的复杂度.仿真和实验结果证明.本文所提出的方法具有较强的鲁棒性,提高了SLAM策略的准确性和实时性.
A Robust Estimator based on Information Potential Energy is proposed to solve the problem of Simultaneous Localization and Map Building (SLAM) in robot indoor.The structured indoor environment can be approximated by line segments.However, in dynamic environment, the data measured by ranging sensor is usually Annihilation in a large number of noise signals.This paper uses the “split-and-merge” method to classify the segments, and according to the performance of information potential energy measure the contribution of each sample data to the information of the line segment. Information optimization theory design estimator, select sample points with large contribution of information as internal points of information extraction line segment parameters, to build local map.Using particle filter to update the map and robot path.Recursive method is used to estimate information potential and reduce The contribution of the sample points to the amount of information is estimated when the complexity of the simulation and experimental results show that the proposed method has strong robustness and improves the accuracy and real-time SLAM strategy.