Simultaneous Localization, Mapping and Detection of Objects for Mobile Robot Based on Information Fusion in Dynamic Environment

Article Preview

Abstract:

This paper presents a novel approach for simultaneous localization, mapping (SLAM) and detection of moving object based on information fusion. We use different information sources, such as laser range scanner, and monocular camera, to improve the ability of distinction between object and background environment. The approach improves the accuracy of SLAM in complex environment, reduces the interference caused by objects, and enhances the practical utility of traditional methods of SLAM. Moreover, the approach expands fields of both research and application of SLAM in combination with target tracking method. Results in real robot experiments show the effectiveness of our method.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

319-322

Citation:

Online since:

July 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] P. J. Besl, N. D. Mckay. A method for registration of 3-D shapes, IEEE Transaction on Pattern Analysis and Machine Intelligence, 14(2): 239-256, (1992).

DOI: 10.1109/34.121791

Google Scholar

[2] R. Chatila, J. P. Laumond. Position referencing and consistent world modeling for mobile robots. In Proc. of the IEEE Int. Conf. on Robotics and Automation), St, Louis, 1985. pp: 138-145.

DOI: 10.1109/robot.1985.1087373

Google Scholar

[3] D. Hahnel, R. Triebel, W. Burgard. Map building with mobile robots in dynamic environments. In Proc. of the IEEE Int. Conf. on Robotics and Automation, Taipei, Taiwan, 2003. pp: 1557-1563.

DOI: 10.1109/robot.2003.1241816

Google Scholar

[4] W. Ming, S. J Ying. Simultaneous Localization, Mapping and Detection of Moving Objects with Mobile Robot in Dynamic Environments. In Proc. of the IEEE int. Conf. on Computer Engineering and Technology. Chengdu, China, 2010. pp: 696-699.

DOI: 10.1109/iccet.2010.5485382

Google Scholar

[5] G. Bradski, A. Kaebler. Learning OpenCV, Beijing: Tsinghua University Press, (2009).

Google Scholar

[6] D. Comaniciu, V. Ramesh, P. Meer. Kernel-based Object tracking. IEEE Transation on Pattern Analysis Machine Intelligence, 2003, 25(5): 564-577.

DOI: 10.1109/tpami.2003.1195991

Google Scholar

[7] Z. Qilong, R. Pless. Extrinsic calibration of a camera and laser range finder. In Proc. of the IEEE Int. Con. on Intelligent Robots and Systems, USA, 2004. pp: 1232-1238.

DOI: 10.1109/iros.2004.1389752

Google Scholar

[8] Y. B. Jean. Camera Calibration Toolbox for Matlab, (2003).

Google Scholar

[9] T. Peynot, A. Kassir. Laser-Camera data discrepancies and reliable perception in outdoor robotics. In Proc. of the IEEE Int. Con. on Intelligent Robots and Systems, Taipei, Taiwan, 2010 pp: 2625-2632.

DOI: 10.1109/iros.2010.5648934

Google Scholar

[10] S. J. Julier, J. Uhlmann. General decentralized data fusion with covariance intersection. Hall D, Llians J. Handbook of multi-sensor data fusion, USA: CRC Press, (2001).

DOI: 10.1201/9781420038545.ch12

Google Scholar

[11] T. Peynot, S. Scheding and S. Terho. The Marulan Data Sets: Multi-Sensor Perception in Natural Environment with Challenging Conditions. International Journal of Robotics Research (IJRR), 29(13): 1602-1607, (2010).

DOI: 10.1177/0278364910384638

Google Scholar