Integrated Autonomous Navigation System and Automatic Large Scale Three Dimensional Map Construction

Yusuke Fujino; Kentaro Kiuchi; Shogo Shimizu; Takayuki Yokota; Yoji Kuroda

doi:10.20965/jrm.2015.p0401

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JRM Vol.27 No.4 pp. 401-409

doi: 10.20965/jrm.2015.p0401

(2015)

Paper:

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Integrated Autonomous Navigation System and Automatic Large Scale Three Dimensional Map Construction

Yusuke Fujino, Kentaro Kiuchi, Shogo Shimizu, Takayuki Yokota, and Yoji Kuroda

Department of Mechanical Engineering, Meiji University
1-1-1 Higashimita, Tama-ku, Kawasaki 214-8571, Japan

Received:

March 16, 2015

Accepted:

June 6, 2015

Published:

August 20, 2015

Keywords:

autonomous navigation robot, human recognition, automatic three dimensional map construction

Abstract

Constructed large-scale 3D map

The method we propose for constructing a large three-dimensional (3D) map uses an autonomous mobile robot whose navigation system enables the map to be constructed. Maps are vital to autonomous navigation, but constructing and updating them while ensuring that they are accurate is challenging because the navigation system usually requires accurate maps. We propose a navigation system that explores areas not explored before. The proposed system mainly uses LIDARs for determining its own position – a process known as localization – or the environment around the robot – a process known as environment recognition – for creating local maps and for avoiding mobile objects – a process known as motion planning. We constructed a detailed 3D map automatically using autonomous driving data to improve navigation accuracy without increasing the operator’s workload, confirming the feasibility of the proposed method through experiments.

Cite this article as:

Y. Fujino, K. Kiuchi, S. Shimizu, T. Yokota, and Y. Kuroda, “Integrated Autonomous Navigation System and Automatic Large Scale Three Dimensional Map Construction,” J. Robot. Mechatron., Vol.27 No.4, pp. 401-409, 2015.

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References

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[3] T. Shioya, K. Kogure, and N. Ohta, “Minimal Autonomous Mover: MG-11 for Tsukuba Challenge,” J. of Robotics and Mechatronics, Vol.26, No.2, pp. 225-235, 2014.
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[5] Q. V. Le, “Building high-level features using large scale unsupervised learning,” Acoustics, 2013.
[6] T. Yokota et al., “Development of the Versatile Autonomous Navigation System,” The 15^th SICE System Integration, Tokyo, 2014 (in Japanese).
[7] S. Shimizu and Y. Kuroda, “High-Speed Registration of Point Clouds by using Dominant Planes,” The 19^th Robotics Symposia, pp. 453-458, 2014 (in Japanese).
[8] C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Trans. on Intelligent Systems and Technology, 2011.
[9] S. Thrun et al., “Stanley: The robot that won the DARPA Grand Challenge,” Proc. of the JFR, Vol.23, No.9, pp. 661-692, 2006.
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[11] S. Koenig and M. Likhachev, “D* Lite,” Proc. of the AAAI, pp. 476-483, 2003.
[12] D. Ferguson, T. M. Howard, and M. Likhachev, “Motion planning in urban environments,” Proc. of the JFR, Vol.25, pp. 939-960, 2008.
[13] . Yokota and Y.Kuroda, “LIDAR-based Human Recognition using Shape Features,” The Robotics and Mechatronics Conf. 2014, 3P1-K04, 2014 (in Japanese).
[14] R. Kummerle, G. Grisetti, H. Strasdat, K. Konolige, and W. Burgard, “g²o: A general framework for graph optimization,” pp. 3607-3613, 2011.
[15] G. Grisetti, R. Kummerle, and C. Stachniss, “A tutorial on graph-based SLAM,” IEEE Intelligent Transportation Systems Magazine, Vol.2, pp. 31-43, 2010.
[16] S. Rusinkiewicz and M. Levoy, “Efficient variants of the ICP algorithm,” Proc. of the Int. Conf. on 3-D Digital Imaging and Modeling (3DIM), pp. 145-152, 2001.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] N. Akai, K. Inoue, and K. Ozaki, “Autonomous navigation based on magnetic and geometric landmarks on environmental structure in real world,” J. of Robotics and Mechatronics, Vol.26, No.2, pp. 158-165, 2014.

[2] [2] J. Eguchi and K. Ozaki, “Development of the Autonomous Mobile Robot for Target-Searching in Urban Areas in the Tsukuba Challenge 2013,” J. of Robotics and Mechatronics, Vol.26, No.2, pp. 166-176, 2014.

[3] [3] T. Shioya, K. Kogure, and N. Ohta, “Minimal Autonomous Mover: MG-11 for Tsukuba Challenge,” J. of Robotics and Mechatronics, Vol.26, No.2, pp. 225-235, 2014.

[4] [4] L. Gomes, “Hidden Obstacles for Google’s Self-Driving Cars,” MIT Technology Review, August 28, 2014.

[5] [5] Q. V. Le, “Building high-level features using large scale unsupervised learning,” Acoustics, 2013.

[6] [6] T. Yokota et al., “Development of the Versatile Autonomous Navigation System,” The 15^th SICE System Integration, Tokyo, 2014 (in Japanese).

[7] [7] S. Shimizu and Y. Kuroda, “High-Speed Registration of Point Clouds by using Dominant Planes,” The 19^th Robotics Symposia, pp. 453-458, 2014 (in Japanese).

[8] [8] C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Trans. on Intelligent Systems and Technology, 2011.

[9] [9] S. Thrun et al., “Stanley: The robot that won the DARPA Grand Challenge,” Proc. of the JFR, Vol.23, No.9, pp. 661-692, 2006.

[10] [10] F. Neuhaus et al., “Terrain drivability analysis in 3D laser range data for autonomous robot navigation in unstructured environments,” Emerging Technologies and Factory Automation (ETFA), pp. 1-4, 2009.

[11] [11] S. Koenig and M. Likhachev, “D* Lite,” Proc. of the AAAI, pp. 476-483, 2003.

[12] [12] D. Ferguson, T. M. Howard, and M. Likhachev, “Motion planning in urban environments,” Proc. of the JFR, Vol.25, pp. 939-960, 2008.

[13] [13] . Yokota and Y.Kuroda, “LIDAR-based Human Recognition using Shape Features,” The Robotics and Mechatronics Conf. 2014, 3P1-K04, 2014 (in Japanese).

[14] [14] R. Kummerle, G. Grisetti, H. Strasdat, K. Konolige, and W. Burgard, “g²o: A general framework for graph optimization,” pp. 3607-3613, 2011.

[15] [15] G. Grisetti, R. Kummerle, and C. Stachniss, “A tutorial on graph-based SLAM,” IEEE Intelligent Transportation Systems Magazine, Vol.2, pp. 31-43, 2010.

[16] [16] S. Rusinkiewicz and M. Levoy, “Efficient variants of the ICP algorithm,” Proc. of the Int. Conf. on 3-D Digital Imaging and Modeling (3DIM), pp. 145-152, 2001.