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:2019,28(7):169-173
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基于RBPF-SLAM算法的研究与实现
王辉1,2, 王品2,3
(1.中国科学院大学, 北京 100049;2.中国科学院 沈阳计算技术研究所 高档数控国家工程研究中心, 沈阳 110168;3.沈阳高精数控智能技术股份有限公司, 沈阳 110168)
Research and Implementation of RBPF-SLAM Algorithm
(1.University of Chinese Academy of Sciences, Beijing 100049, China;2.National Engineering Research Center for High-end CNC, Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;3.Shenyang High Precision Digital Control Intelligent Technology Co. Ltd., Shenyang 110168, China)
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投稿时间:2019-01-10    修订日期:2019-02-19
中文摘要: 同时定位与地图创建(the simultaneous localization and mapping,SLAM)是机器人领域的难点问题,目前广泛采用Rao-Blackwellized Particle Filters (RBPF)算法解决该问题.在传统的RBPF算法实现中构建的高误差建议分布会采样计算大量粒子来拟合目标分布,频繁的重采样步骤导致粒子逐渐耗散,浪费大量计算资源.在本文中通过把运动模型信息与观测信息相结合优化建议分布,减少采样粒子数量,引入自适应重采样方法减少重采样步骤.在算法的实现时使用树形数据结构存储环境地图,实验结果表明,该改进算法可以显著计算效率,减小存储消耗,构建地图更为精确.
Abstract:The Simultaneous Localization And Mapping (SLAM) is a difficult problem in the field of robotics. Rao-Blackwellized particle filters algorithm is widely used to solve this problem. In the traditional implementation, the proposed distribution with high error will calculate a large number of sampled particles to fit the target distribution. Frequent resampling steps will lead to gradual dissipation of particles and waste a lot of computing resources. In this study, the motion model and observation information are combined to optimize the proposed distribution, reduce the number of sampled particles, and the adaptive resampling method is introduced to reduce the steps of resampling. In the implementation of the algorithm, the tree data structure is used to store the environment map. The experimental results show that the improved algorithm can significantly improve the computational efficiency, reduce the storage consumption, and build more accurate map.
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基金项目:国家重大科技专项(2018ZX04035001)
引用文本:
王辉,王品.基于RBPF-SLAM算法的研究与实现.计算机系统应用,2019,28(7):169-173
WANG Hui,WANG Pin.Research and Implementation of RBPF-SLAM Algorithm.COMPUTER SYSTEMS APPLICATIONS,2019,28(7):169-173

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