MCL with sensor fusion based on a weighting mechanism versus a particle generation approach

被引：0

作者：

Perea, Daniel ^{[1
]}

Hernandez-Aceituno, Javier ^{[1
]}

Morell, Antonio ^{[1
]}

Toledo, Jonay ^{[1
]}

Hamilton, Alberto ^{[1
]}

Acosta, Leopoldo ^{[1
]}

机构：

[1] Univ La Laguna, Dept Ingn Sistemas & Automat & Arquitectura Tecno, Tenerife 38203, Spain

来源：

2013 16TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS - (ITSC) | 2013年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The combined action of several sensing systems, so that they are able to compensate the technical flaws of each other, is common in robotics. Monte Carlo Localization (MCL) is a popular technique used to estimate the pose of a mobile robot, which allows the fusion of heterogeneous sensor data. Several sensor fusion schemes have been proposed which include sensors like GPS to improve the performance of this algorithm. In this paper, an Adaptive MCL algorithm is used to combine data from wheel odometry, an inertial measurement unit, a global positioning system and laser scanning. A particle weighting model which integrates GPS measurements is proposed, and its performance is compared with a particle generation approach. Experiments were conducted on a real robotic car within an urban environment.

引用

页码：166 / 171

页数：6

共 21 条

[1] [Anonymous], 2005, PROBABILISTIC ROBOTI, DOI DOI 10.5555/1121596
[2] Cappelle Cindy, 2007, 2007 IEEE Intelligent Transportation Systems Conference, P1102, DOI 10.1109/ITSC.2007.4357697
[3] Chang H., 2006, INT JOINT C OCT, P3626
[4] Chang H, 2006, IEEE INT C EMERG, P510
[5] Adapting the sample size in particle filters through KLD-sampling
Fox, D
[J]. INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 2003, 22 (12) : 985 - 1003
[6] Fox D, 1999, SIXTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-99)/ELEVENTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE (IAAI-99), P343
[7] Frontoni Emanuele, 2010, 2010 IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA 2010), P428, DOI 10.1109/MESA.2010.5551994
[8] A GPS and Laser-based Localization for Urban and Non-Urban Outdoor Environments
Hentschel, Matthias
Wulf, Oliver
Wagner, Bernardo
[J]. 2008 IEEE/RSJ INTERNATIONAL CONFERENCE ON ROBOTS AND INTELLIGENT SYSTEMS, VOLS 1-3, CONFERENCE PROCEEDINGS, 2008, : 149 - 154
[9] ON INFORMATION AND SUFFICIENCY
KULLBACK, S
LEIBLER, RA
[J]. ANNALS OF MATHEMATICAL STATISTICS, 1951, 22 (01): : 79 - 86
[10] Lijun Wei, 2011, 2011 15th International Conference on Advanced Robotics, P192, DOI 10.1109/ICAR.2011.6088617

← 1 2 3 →