A fusion-based Localization Method of Mobile Robot With Equality Geometric Constraint

被引：0

作者：

Wang, Zhongli ^{[1
,2
,3
]}

Wu, Xian ^{[1
,3
]}

Cai, Baigen ^{[1
,3
]}

Tao, Chuanqi ^{[4
]}

Zhang, Zhiyi ^{[4
]}

Wang, Yinling ^{[4
]}

机构：

[1] Beijing Jiao Tong Univ, Sch Elect Informat & Engn, Beijing 100044, Peoples R China

[2] Harbin Inst Technol, State Key Lab Robot & Syst, Harbin 150001, Peoples R China

[3] Beijing Engn Res Ctr EMC & GNSS Technol Rail Tran, Beijing 100044, Peoples R China

[4] CSR Qindao Sifang Co Ltd, Qingdao 266111, Peoples R China

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON REAL-TIME COMPUTING AND ROBOTICS (IEEE RCAR) | 2016年

关键词：

Localization; equality constraint; extended Kalman filter; multi-sensor data fusion;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

GPS/ODO fusion-based localization is always used for mobile robot in outdoor environment. This approach takes advantage of short-term accuracy of odometry measurement and the long-term correction by the GPS unit. Generally, to improve the localization accuracy, precise system model and sensor noise model are probed. In this paper, by taking the linear and nonlinear equality state constraints into the EKF-based fusion framework with the method of perfect measurement and estimate projection, the performance of fusion results can be greatly improved. The modeling process is introduced and two simulation cases have been conducted, which show the positioning accuracy of the mobile robot can he significantly improved after the introduction of the equivalent constraint. Theoretically, this implementation allows us to define additional adjustments to improve the overall behavior of the filter.

引用

页码：156 / 162

页数：7

共 20 条

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