Development of Method Using a Combination of DGPS and Scan Matching for the Making of Occupancy Grid Maps for Localization

Junji Eguchi; Koichi Ozaki

doi:10.20965/jrm.2013.p0506

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JRM Vol.25 No.3 pp. 506-514

doi: 10.20965/jrm.2013.p0506

(2013)

Paper:

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Development of Method Using a Combination of DGPS and Scan Matching for the Making of Occupancy Grid Maps for Localization

Junji Eguchi and Koichi Ozaki

Utsunomiya University, 7-1-2 Yoto, Utsunomiya-city, Tochigi 321-8585, Japan

Received:

October 19, 2012

Accepted:

April 20, 2013

Published:

June 20, 2013

Keywords:

autonomous mobile robot, SLAM, GPS, laser scanner, particle filter

Abstract

This paper describes a method of making an occupancy grid map through the combined use of DGPS and scan matching. In outdoor environments such as city areas, high-accuracy localization is required for autonomous navigation. Scan matching with a laser scanner and an occupancy grid map consisting of precise structure information on the environment is one of the most accurate localization methods. However, mismatching on the map sometimes occurs, resulting in the robot losing its own position. Although a GPS device, an absolute positioning device, is valid for estimating position and attitude to a certain degree of accuracy, GPS often obtains erroneous positions for multipath problems which occur around tall buildings. In order to estimate the position and attitude of robots more stably, the authors have developed a method of making an occupancy grid map, which corresponds to DGPS directions and has an accurate shape, by using of some accurate DGPS measurement points and the SLAM method. In autonomous navigation, the robot trajectory is estimated using the particle filter method, evaluation and resampling are done using the two ways mentioned above, and attitude is calculated using DGPS measurement points and the result of scan matching. In this paper, the performance of the map-making method and localization method for autonomous navigation is shown through experiments which are evaluated as to the accuracy of the map in an actual environment.

Cite this article as:

J. Eguchi and K. Ozaki, “Development of Method Using a Combination of DGPS and Scan Matching for the Making of Occupancy Grid Maps for Localization,” J. Robot. Mechatron., Vol.25 No.3, pp. 506-514, 2013.

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References

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[2] K. Irie, T. Yoshida, E. Koyanagi, and M. Tomono, “A Localization Method Using Gyro-assisted Odometry and 3D Laser Scanner Considering Gyro Drift Error,” 2010 JSME Conf. on Robotics and Mechatronics, 1A1-E01, 2010 (in Japanese).
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[9] M. Kitamura, T. Suzuki, Y. Amano, and T. Hashizume, “Improvement of GPS and GLONASS Positioning Accuracy by Multipath Mitigation Using Omnidirectional Infrared Camera,” J. of Robotics and Mechatronics, Vol.23, No.6 pp. 1125-1131, 2011.
[10] Y. Hara, H. Kawata, A. Ohya, and S.Yuta, “Map Building for Mobile Robots using a SOKUIKI Sensor – Robust Scan Matching using Laser Reflectance Intensity –,” SICE-ICASE Int. Joint Conf. 2006, SE14-4, 2006.
[11] H. Date and Y. Takita, “Real World Experiments of Autonomous Mobile Robot Smart Dump – Influence and Countermeasure of Human Crowd Behavior in a Pedestrian Environment –,” J. of Robotics Society of Japan, Vol.30, No.3, pp. 305-313, 2012 (in Japanese).
[12] Y. Morales, E. Takeuchi, and T. Tsubouchi, “Vehicle Localization in Outdoor Woodland Environment with sensor fault detection,” Proc. of the ICRA 2008, pp. 449-454, 2008.
[13] M. Montemerlo, S. Thun, D. Koller, and B. Wegbreit, “FastSLAM: A factored solution to simultaneous mapping and localization,” in: Proc. National Conf. on Artificial Intelligence (AAAI), 2002.

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

[1] [1] S. Yuta, M. Mizukawa, and H. Hashimoto, “Tsukuba Challenge: The Purpose and Results,” J. of the Society of Instrument and Control Engineers, Vol.49, No.9, pp. 572-578, 2010 (in Japanese).

[2] [2] K. Irie, T. Yoshida, E. Koyanagi, and M. Tomono, “A Localization Method Using Gyro-assisted Odometry and 3D Laser Scanner Considering Gyro Drift Error,” 2010 JSME Conf. on Robotics and Mechatronics, 1A1-E01, 2010 (in Japanese).

[3] [3] M. Yokozuka, Y. Suzuki, T. Takei, N. Hashimoto, and O. Matsumoto, “Auxiliary Particle Filter Localization for Intelligent Wheelchair Systems in Urban Environments,” J. of Robotics and Mechatronics, Vol.22, No.6, pp. 758-765, 2010.

[4] [4] T. Tomizawa, S. Muramatsu, M. Sato, M. Hirai, S. Kudoh, and T. Suehiro, “Development of an Intelligent Senior-Car in a Pedestrian Walkway,” Advanced Robotics, Vol.26, No.14, 2012.

[5] [5] H. Date and Y. Takita, “Real world experiments of an autonomous mobile robot in the pedestrian environment,” in: Proc. 5th Int. Conf. on Automation, Robotics and Applications, pp. 413-418, Wellington, New Zealand, 2011.

[6] [6] T. Suzuki et al., “The Possibility of the Precise Positioning andMultipath Error Mitigation in the Real-time,” The 2004 Int. Symposium on GNSS/GPS, 2004.

[7] [7] K. Ohno, T. Tsubouchi, B. Shigematsu, and S. Yuta, “Differential GPS and odometry-based outdoor navigation of a mobile robot,” Advanced Robotics, Vol.18, No.6, pp. 611-635, 2004.

[8] [8] E. Takeuchi, M. Yamazaki, K. Ohno, and S. Tadokoro, “GPS Measurement Model with Satellite Visibility using 3D Map for Particle Filter,” 2011 IEEE Int. Conf. on Robotics and Biomimetics (ROBIO 2011), 2011.

[9] [9] M. Kitamura, T. Suzuki, Y. Amano, and T. Hashizume, “Improvement of GPS and GLONASS Positioning Accuracy by Multipath Mitigation Using Omnidirectional Infrared Camera,” J. of Robotics and Mechatronics, Vol.23, No.6 pp. 1125-1131, 2011.

[10] [10] Y. Hara, H. Kawata, A. Ohya, and S.Yuta, “Map Building for Mobile Robots using a SOKUIKI Sensor – Robust Scan Matching using Laser Reflectance Intensity –,” SICE-ICASE Int. Joint Conf. 2006, SE14-4, 2006.

[11] [11] H. Date and Y. Takita, “Real World Experiments of Autonomous Mobile Robot Smart Dump – Influence and Countermeasure of Human Crowd Behavior in a Pedestrian Environment –,” J. of Robotics Society of Japan, Vol.30, No.3, pp. 305-313, 2012 (in Japanese).

[12] [12] Y. Morales, E. Takeuchi, and T. Tsubouchi, “Vehicle Localization in Outdoor Woodland Environment with sensor fault detection,” Proc. of the ICRA 2008, pp. 449-454, 2008.

[13] [13] M. Montemerlo, S. Thun, D. Koller, and B. Wegbreit, “FastSLAM: A factored solution to simultaneous mapping and localization,” in: Proc. National Conf. on Artificial Intelligence (AAAI), 2002.