融入二维码信息的自适应蒙特卡洛定位算法

doi:10.11772/j.issn.1001-9081.2018091910

计算机应用 ›› 2019, Vol. 39 ›› Issue (4): 989-993.DOI: 10.11772/j.issn.1001-9081.2018091910

融入二维码信息的自适应蒙特卡洛定位算法

胡章芳¹, 曾林全¹, 罗元¹, 罗鑫¹, 赵立明²

1. 重庆邮电大学光电工程学院, 重庆 400065;
2. 重庆邮电大学先进制造工程学院, 重庆 400065

收稿日期:2018-09-13 修回日期:2018-11-21 出版日期:2019-04-10 发布日期:2019-04-10
通讯作者: 曾林全
作者简介:胡章芳(1969-),女,重庆人,教授,硕士,主要研究方向:光电信息处理;曾林全(1993-),女,重庆人,硕士研究生,主要研究方向:机器人导航;罗元(1972-),女,湖北宜昌人,教授,博士,主要研究方向:数字图像处理;罗鑫(1999-),男,重庆人,主要研究方向:视觉导航;赵立明(1981-),男,河北秦皇岛人,讲师,博士,主要研究方向:机器视觉。
基金资助:
重庆市科委基础与前沿研究计划项目（cstc2016jcyjA0537）。

Adaptive Monte-Carlo localization algorithm integrated with two-dimensional code information

HU Zhangfang¹, ZENG Linquan¹, LUO Yuan¹, LUO Xin¹, ZHAO Liming²

1. School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2018-09-13 Revised:2018-11-21 Online:2019-04-10 Published:2019-04-10
Supported by:
This work is partially supported by the Basic and Frontier Research Project of Chongqing Municipal Science and Technology Commission (cstc2016jcyjA0537).

摘要/Abstract

摘要： 蒙特卡洛定位（MCL）算法存在计算量大、定位精度差的问题，由于二维码具有携带信息的多样性、二维码识别的方便性与易用性的特点，提出一种融入二维码信息的自适应蒙特卡洛定位算法。首先，利用二维码提供的绝对位置信息修正里程计模型的累计误差后进行采样；然后，采用激光传感器提供的观测模型确定粒子的重要性权重；最后，因为重采样部分采用固定样本集会导致大计算量，所以利用Kullback-Leibler距离（KLD）进行重采样，根据粒子在状态空间的分布情况自适应调整下一次迭代所需粒子数，从而减小计算量。基于移动机器人进行的实验结果表明，改进算法与传统蒙特卡洛算法相比定位精度提高了15.09%，时间缩短了15.28%。

关键词: 蒙特卡洛定位, 里程计运动模型, 观测模型, 二维码, Kullback-Leibler距离采样

Abstract: Monte Carlo Localization (MCL) algorithm has many problems such as large computation and poor positioning accuracy. Because of the diversity of information carried by two-dimensional code and usability and convenience of two-dimensional code recognition, an adaptive MCL algorithm integrated with two-dimensional code information was proposed. Firstly, the cumulative error of odometer model was corrected by absolute position information provided by two-dimensional code and then sampling was performed. Sencondly, the measurement model provided by laser sensor was used to determine the importance weights of the particles. Finally, as fixed sample set used in the resampling part caused large computation, Kullback-Leibler Distance (KLD) was utilized in resampling to reduce the computation by adaptively adjusting the number of particles required for the next iteration according to the distribution of particles in state space. Experimental result on the mobile robot show that the proposed algorithm improves the localization accuracy by 15.09% and reduces the localization time by 15.28% compared to traditional Monte-Carlo algorithm.

Key words: Monte-Carlo Localization (MCL), odometer motion model, measurement model, two-dimensional coding, Kullback-Leibler Distance (KLD) sampling

中图分类号:

TP242.6

胡章芳, 曾林全, 罗元, 罗鑫, 赵立明. 融入二维码信息的自适应蒙特卡洛定位算法[J]. 计算机应用, 2019, 39(4): 989-993.

HU Zhangfang, ZENG Linquan, LUO Yuan, LUO Xin, ZHAO Liming. Adaptive Monte-Carlo localization algorithm integrated with two-dimensional code information[J]. Journal of Computer Applications, 2019, 39(4): 989-993.

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融入二维码信息的自适应蒙特卡洛定位算法

Adaptive Monte-Carlo localization algorithm integrated with two-dimensional code information

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