Accurate Localization for Making Maps to Mobile Robots Using Odometry and GPS Without Scan-Matching

Masashi Yokozuka; Osamu Matsumoto

doi:10.20965/jrm.2015.p0410

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JRM Vol.27 No.4 pp. 410-418

doi: 10.20965/jrm.2015.p0410

(2015)

Paper:

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Accurate Localization for Making Maps to Mobile Robots Using Odometry and GPS Without Scan-Matching

Masashi Yokozuka and Osamu Matsumoto

National Institute of Advanced Industrial Science and Technology (AIST)
1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan

Received:

March 2, 2015

Accepted:

May 14, 2015

Published:

August 20, 2015

Keywords:

mobile robot, mapping, localization, GPS, bundle adjustment

Abstract

Comparison of mapping results

This paper studies an accurate localization method to make maps for mobile robots using odometry and a global positioning system (GPS) without scan matching. We investigate requirements for GPS accuracy in map-making. To generate accurate maps, SLAM techniques such as scan matching are used to obtain accurate positions. Scan matching is unstable, however, in complex environments and has a high computation cost. To avoid these problems, we studied accurate localization without scan matching. Loop closing is an important property in generating consistent maps. Inconsistencies in maps prevent correct routes to destinations from being generated. Basically, our method adds scan data to a map along a trajectory given by odometry. Odometry accumulates errors due, e.g., to wheel slippage or wheel diameter variations. To remove this accumulated error, we used bundle adjustment, introducing two types of processing. The first is a simple manual input moving a robot to a same position at start and end. This is equal that a robot returns to a start position at end. The second process uses a GPS device to improve map accuracy. Results of experiments showed that an accurate map is generated by using wheel-encoder odometry and a low-cost GPS device. Results were evaluated using a real-time kinematic (RTK) GPS device whose accuracy is within a few centimeters.

Cite this article as:

M. Yokozuka and O. Matsumoto, “Accurate Localization for Making Maps to Mobile Robots Using Odometry and GPS Without Scan-Matching,” J. Robot. Mechatron., Vol.27 No.4, pp. 410-418, 2015.

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References

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[7] V. Indelman, R. Roberts, C. Beall, and F. Dellaert, “Incremental Light Bundle Adjustment,” Proc. of British Machine Vision Conf., 2012.
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[17] S. Muramatsu, T. Tomizawa, S. Kudoh, and T. Suehiro, “Development of Intelligent Mobile Cart in a Crowded Environment – Robust Localization Technique with Unknown Objects –,” J. of Robotics and Mechatronics, Vol.26, No.2, 2014.

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

[1] [1] G. Klein and D. Murray, “Parallel Tracking and Mapping for Small AR Workspaces,” Proc. of Int. Symposium on Mixed and Augmented Reality, 2007.

[2] [2] N. Snavely, S, M. Seitz, and R. Szeliski, “Photo Tourism: Exploring image collections in 3D,” ACM Trans. on Graphics (Proc. of SIGGRAPH 2006), 2006.

[3] [3] N. Snavely, S, M. Seitz, and R. Szeliski, “Modeling the World from Internet Photo Collections,” Int. J. of Computer Vision, 2007.

[4] [4] F. Luand and E. Milios, “Globally consistent range scan alignment for environment mapping,” Autonomous Robots, Vol.4, pp. 333-349, 1997.

[5] [5] S. Thrun, W. Burgard, and D. Fox, “Probabilistic Robotics,” The MIT Press, 2005.

[6] [6] G. Sibley, C. Mei, I. Reid, and P. Newman, “Sparser Relative Bundle Adjustment (SRBA): constant-time maintenance and local optimization of arbitrarily large maps,” Proc. of Int. Conf. on Robotics and Automation, 2013.

[7] [7] V. Indelman, R. Roberts, C. Beall, and F. Dellaert, “Incremental Light Bundle Adjustment,” Proc. of British Machine Vision Conf., 2012.

[8] [8] A. L. Rodrlguez, P. E. Lopez-de-Teruel, and A. Ruiz, “GEA Optimization for Live Structureless Motion Estimation,” Proc. of Int. Conf. on Computer Vision, 2011.

[9] [9] A. L. Rodrlguez, P. E. Lopez-de-Teruel, and A. Ruiz, “Reduced Epipolar Cost for Accelerated Incremental SfM,” Proc. of Int. Conf. on Computer Vision and Pattern Recognition, 2011.

[10] [10] J. Wang and E. Olson, “Robust Pose Graph Optimization Using Stochastic Gradient Descent,” Proc. of Int. Conf. on Robotics and Automation, 2014.

[11] [11] E. Olson, J. Leonard, and S. Teller, “Fast iterative optimization of pose graphs with poor initial estimates,” Proc. of Int. Conf. on Robotics and Automation, 2006.

[12] [12] B. Peasley and S. Birchfield, “Fast and Accurate PoseSLAM by Combining Relative and Global State Spaces,” Proc. of Int. Conf. on Robotics and Automation, 2014.

[13] [13] R. Kummerle, G. Grisetti, H. Strasdat, K. Konolige, and W. Burgard, “g2o: A general framework for graph optimization,” Proc. of Int. Conf. on Robotics and Automation, 2011.

[14] [14] K. Konolige, G. Grisetti, R. Kummerle, W. Burgard, B. Limketkai, and R. Vincent, “Sparse pose adjustment for 2D mapping,” Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2010.

[15] [15] 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, 2014.

[16] [16] S. Rahok, H. Oneda, A. Tanaka, and K. Ozaki, “A Robot Navigation Method for Mobile Robots in Real-World Environment,” J. of Robotics and Mechatronics, Vol.26, No.2, 2014.

[17] [17] S. Muramatsu, T. Tomizawa, S. Kudoh, and T. Suehiro, “Development of Intelligent Mobile Cart in a Crowded Environment – Robust Localization Technique with Unknown Objects –,” J. of Robotics and Mechatronics, Vol.26, No.2, 2014.