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基于多分辨率粒子滤波的全局协同定位方法
Alternative TitleMulti-resolution and particle filter based global cooperated localization method
殷鹏1,2,3; 何玉庆1,2; 韩建达1,2; 徐卫良1,2,4
Department机器人学研究室
Source Publication中国科学:技术科学
ISSN1674-7259
2019
Volume49Issue:1Pages:87-96
Indexed ByEI ; CSCD
EI Accession number20192006920097
CSCD IDCSCD:6411391
Contribution Rank1
Funding Organization国家自然科学基金(批准号: U1508208, U1608253, 61473282)
Keyword协同定位 迭代最近邻 粒子滤波 GPS缺失
Abstract野外环境下,多类型机器人协同合作可以克服单一类型机器人(如无人车、无人机等)在环境建模任务中的视角、尺度方面的问题,进而提高整体编队系统的环境感知与决策控制能力.而在多类型机器人协作系统中,协作定位是协同合作的关键难题之一,也是进行编队建模与编队控制的基础.在GPS缺失环境下,由于传感器类型的不同,非结构化的环境特征,视角的不同而导致基于匹配的多机器人定位方法无法实现有效稳定的定位.本文提出了一种基于多分辨率最近邻匹配和粒子滤波的协同定位方法,可以在初始相对位置未知的情况下,进行全局范围内的协同定位.本文采用了一种针对粒子匹配精度以及匹配效率的评估方法,并根据粒子评估结果进行粒子权重更新,地图更新以及粒子数目更新,以平衡在定位过程中粒子对状态空间的描述和定位效率.同时,针对于粒子退化或者粒子收敛速度过慢的问题,采用了基于分辨率等级和粒子匹配精度和匹配效率的自适应调整方法.最后结合具体平台,本方法在野外水湾环境进行了基于无人船与无人机的协同定位实验,实验结果表明本方法有效解决多机器人在GPS缺失下的协同定位问题.
Other AbstractCooperation & coordination among heterogeneous robots (e.g., UAV & UGV, UAV & USV) have obtained extensive attentions due to its greatly enhanced perception ability, especially when facing complicated and unstructured outdoor environments. For multi-type robots cooperation task in the GPS-denied environment, cooperate localization is one of the most important task with great challenges which combine the aspects of data collection of different type sensors, adaption to the environment feature and perspective variation. In this paper, we proposed a multi-resolution enhanced cooperated localization method for the multi-type robots task. Based on multiresolution iterative closest point (MRICP) and particle filter (PF), the proposed method could achieve a fast cooperation localization even without a given initial pose estimation. Firstly, a new registration evaluation function is introduced to value the registration efficiency and registration tendency. Secondly, to handle the posterior distribution problem in the PF based global localization, a map updater model is taken to control map scale, resolution level and the particles size to balance the registration efficiency and accuracy. Thirdly, a self-adoption particle resample scheme could deal with the sample impoverishment/degeneracy problem. Experiments with an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) are conducted for cooperate localization test at a water bay, and the results are analyzed to verify the feasibility and validity of the proposed method.
Language中文
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24150
Collection机器人学研究室
Corresponding Author韩建达
Affiliation1.中国科学院沈阳自动化研究所机器人学国家重点实验室
2.中国科学院机器人与智能制造创新研究院
3.中国科学院大学
4.Department of Mechanical Engineering,University of Auckland
Recommended Citation
GB/T 7714
殷鹏,何玉庆,韩建达,等. 基于多分辨率粒子滤波的全局协同定位方法[J]. 中国科学:技术科学,2019,49(1):87-96.
APA 殷鹏,何玉庆,韩建达,&徐卫良.(2019).基于多分辨率粒子滤波的全局协同定位方法.中国科学:技术科学,49(1),87-96.
MLA 殷鹏,et al."基于多分辨率粒子滤波的全局协同定位方法".中国科学:技术科学 49.1(2019):87-96.
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