DOI QR코드

DOI QR Code

A New Method for Relative/Quantitative Comparison of Map Built by SLAM

SLAM으로 작성한 지도 품질의 상대적/정량적 비교를 위한 방법 제안

  • Received : 2014.05.28
  • Accepted : 2014.10.02
  • Published : 2014.11.28

Abstract

By a SLAM (simultaneous localization and mapping) method, we get a map of an environment for autonomous navigation of a robot. In this case, we want to know how accurate the map is. Or we want to know which map is more accurate when different maps can be obtained by different SLAM methods. So, several methods for map comparison have been studied, but they have their own drawbacks. In this paper, we propose a new method which compares the accuracy or error of maps relatively and quantitatively. This method sets many corresponding points on both reference map and SLAM map, and computes the translational and rotational values of all corresponding points using least-squares solution. Analyzing the standard deviations of all translational and rotational values, we can know the error of two maps. This method can consider both local and global errors while other methods can deal with one of them, and this is verified by a series of simulations and real world experiments.

Keywords

References

  1. G. Zhang, and I. H. Suh, "Loop Closure in a Line-Based SLAM", Journal of Korea Robotics Society, vol. 7, issue 2, pp. 120-128, June, 2012. https://doi.org/10.7746/jkros.2012.7.2.120
  2. C. Nam, J. Kang and N. L. Doh, "A New Observation Model to Improve the Consistency of EKF-SLAM Algorithm in Large-scale Environments", Journal of Korea Robotics Society, vol. 7, issue 1, pp. 29-34, March, 2012. https://doi.org/10.7746/jkros.2012.7.1.029
  3. M. Dissanayake, P. Newman, S. Clark, H. Durrant-Whyte, and M. Csobra, "A solution to the simultaneous localization and mapping (slam) problem", IEEE Transactions on Robotics and Automation, vol. 17, issue 3, pp. 229-241, June, 2001. https://doi.org/10.1109/70.938381
  4. G. Grisetti, C. Stachniss, and W. Burgard, "Improving grid-based SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling", Proceedings of IEEE ICRA 2005, pp. 2432-2437, April, 2005.
  5. F. Lu, and E. Milios, "Globally consistent range scan alignment for environment mapping", Journal of Autonomous Robots, vol. 4, issue 4, pp. 333-349, Oct., 1997. https://doi.org/10.1023/A:1008854305733
  6. O. Wulf, A. Nuchter, J. Hertzberg, and B. Wagner, "Benchmarking urban six-degree-of-freedom simultaneous localization and mapping", Journal of Field Robotics, vol. 25, issue 3, pp. 148-163, March, 2008. https://doi.org/10.1002/rob.20234
  7. R, Iyekkettem, and K. Hirasawa, "A comparison of SLAM implementations for indoor mobile robots", Proceedings of IEEE/RSJ IROS 2007, pp. 1479-1484, Oct. 2007.
  8. R. Kummerle, B. Steder, C. Dornhege, M. Ruhnke, G. Grisetti, C. Stachniss, and A. Kleiner, "On measuring the accuracy of SLAM algorithms", Autonomous Robots, vol. 27, issue 4, pp. 387-407, Nov., 2009. https://doi.org/10.1007/s10514-009-9155-6
  9. B. Balaguer, S. Carpin, and S. Balakirsky, "Toward quantitative comparisons of robot algorithms: Experiences with SLAM in simulation and real world systems", in IROS 2007 workshop, Oct., 2007.
  10. F. Amigoni, S. Gasparini, and M. Gini, "Good experimental methodologies for robotic mapping: A proposal", Proceedings of IEEE ICRA 2007, pp. 4176-4181, April, 2007.
  11. F. Lu, and E. Milios, "Robot pose estimation in unknown environments by matching 2D range scans", Journal of Intelligent and Robotic Systems, vol. 18, issue 3, pp. 249-275, March, 1997. https://doi.org/10.1023/A:1007957421070
  12. http://www.neatorobotics.com/robot-vacuum/xv/, 2014. 09. 13
  13. K. Konolige, J. Augenbraun, N. Donaldson, C. Fiebig, and P. Shah, "A Low-Cost Laser Distance Sensor", Proceedings of IEEE ICRA 2008, pp. 3002-3008, May, 2008.
  14. http://wiki.ros.org/gmapping, 2013.12.09

Cited by

  1. 라이다 점군 밀도에 강인한 맵 오차 측정 기구 설계 및 알고리즘 vol.16, pp.3, 2014, https://doi.org/10.7746/jkros.2021.16.3.189