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Two different tools for three-dimensional mapping: DE-based scan matching and feature-based loop detection

Published online by Cambridge University Press:  19 July 2013

Fernando Martín ,
Rudolph Triebel ,
Luis Moreno  and
Roland Siegwart
Fernando Martín*
Affiliation:
Systems Engineering and Automation, Carlos III University, Madrid, Spain
Rudolph Triebel
Affiliation:
Autonomous Systems Lab, ETH Zurich, Switzerland
Luis Moreno
Affiliation:
Systems Engineering and Automation, Carlos III University, Madrid, Spain
Roland Siegwart
Affiliation:
Autonomous Systems Lab, ETH Zurich, Switzerland
*
*Corresponding author. E-mail: fmmonar@ing.uc3m.es
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Summary

An autonomous robot must obtain information about its surroundings to accomplish multiple tasks that are greatly improved when this information is efficiently incorporated into a map. Some examples are navigation, manipulation, localization, etc. This mapping problem has been an important research area in mobile robotics during last decades. It does not have a unique solution and can be divided into multiple sub-problems. Two different aspects of the mobile robot mapping problem are addressed in this work. First, we have developed a Differential Evolution-based scan matching algorithm that operates with high accuracy in three-dimensional environments. The map obtained by an autonomous robot must be consistent after registration. It is basic to detect when the robot is navigating around a previously visited place in order to minimize the accumulated error. This phase, which is called loop detection, is the second aspect studied here. We have developed an algorithm that extracts the most important features from two different three-dimensional laser scans in order to obtain a loop indicator that is used to detect when the robot is visiting a known place. This approach allows the introduction of very different characteristics in the descriptor. First, the surface features include the geometric forms of the scan (lines, planes, and spheres). Second, the numerical features are values that describe several numerical properties of the measurements: volume, average range, curvature, etc. Both algorithms have been tested with real data to demonstrate that these are efficient tools to be used in mapping tasks.

Keywords

Loop detectionScan matching6D SLAMDifferential evolutionFeature descriptor
Type
Articles
Information
Robotica , Volume 32 , Issue 1 , January 2014 , pp. 19 - 41
Copyright
Copyright © Cambridge University Press 2013 

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References

1.Bentley, J. L., “Multidimensional binary search trees used for associative searching,” Commun. ACM 18, 509–517 (1975).CrossRefGoogle Scholar
2.Besl, P. J. and McKay, N. D., “A method for registration of 3D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14 (2), 239–256 (1992).CrossRefGoogle Scholar
3.Biber, P. and Straβer, W., “The Normal Distributions Transform: A New Approach to Laser Scan Matching,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'03), Las Vegas, USA (2003).Google Scholar
4.Bosse, M. and Roberts, J., “Historgram Matching and Global Initialization for Laser-Only SLAM in Large Unstructured Environments,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'07), Roma, Italy (Apr. 10–14, 2007).Google Scholar
5.Bosse, M. and Zlot, R., “Map matching and data association for large-scale 2D laser scan-based SLAM,” Int. J. Robot. Res. 27, 667–691 (2008).CrossRefGoogle Scholar
6.Cole, D. M. and Newman, P. M., “Using Laser Range Data for 3D SLAM in Outdoor Environments,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'06), Orlando, USA (2006).Google Scholar
7.Cummings, M. and Newman, P., “FAB-MAP: Probabilistic localization and mapping in the space of appearance,” Int. J. Robot. Res. 27 (6), 647–665 (Jun. 2008).CrossRefGoogle Scholar
8.Diosi, A. and Kleeman, L., “Laser Scan Matching in Polar Coordinates with Application to SLAM,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'05), Edmonton, Canada (2005).Google Scholar
9.Freund, Y. and Schaphire, R. E, “A decision-theoretic generalization of on-line learning and an application to boosting,” J. Comput. Syst. Sci. 55, 119–139 (1997).CrossRefGoogle Scholar
10.Früh, C. and Zakhor, A., “3D Model Generation for Cities Using Aerial Photographs and Ground Level Laser Scans. Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR'01), Kauai, Hawaii, USA (2001).Google Scholar
11.Granström, K., Schön, T. B, Nieto, J. I, and Ramos, F. T, “Learning to close loops from range data,” The International Journal of Robotics Research, 30 (14), 1728–1754 (2011).CrossRefGoogle Scholar
12.Grisetti, G., Stachniss, C., Grzonka, S. and Burgard, W., “A Tree Parameterization for Efficiently Computing Maximum Likelihood Maps Using Gradient Descent,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'08), Pasadena, CA, USA (2008).Google Scholar
13.Hähnel, D., Burgard, W. and Thrun, S., “Learning compact 3D models of indoor and outdoor environments with a mobile robot,” Robot. Auton. Syst. 44, 15–27 (2003).CrossRefGoogle Scholar
14.Leonard, J. J. and Durrant-Whyte, H., “Mobile robot localization by tracking geometric beacons,” IEEE Trans. Robot. Autom. 7, 376–382 (1991).CrossRefGoogle Scholar
15.Lu, F. and Milios, E., “Robot pose estimation in unknown environments by matching 2D range scans,” J. Intell. Robot. Syst. 20, 249–275 (1997).CrossRefGoogle Scholar
16.Magnusson, M. and Ducket, T., “A Comparison of 3D Registration Algorithms for Autonomous Underground Mining Vehicles,” Proceedings of the Second European Conference on Mobile Robotics, Ancona, Italy (2005).Google Scholar
17.Magnusson, M., Andreassonand, H., Nüchter, A. and Lilienthal, Achim J., “Automatic appearance-based loop detection from three-dimensional laser data using the normal distributions transform,” J. Field Robot. 26, 892–914 (2009).CrossRefGoogle Scholar
18.Martín, F., Moreno, L., Garrido, S. and Blanco, D., “High-accuracy global localization filter for three-dimensional environments,” Robotica 30, 363–378 (2011).CrossRefGoogle Scholar
19.Moreno, L., Garrido, S. and Muñoz, M. L., “Evolutionary filter for robust mobile robot localization,” Robot. Auton. Syst. 54 (7), 590–600 (2006).CrossRefGoogle Scholar
20.Nüchter, A., Lingemann, K. and Hertzberg, J., “6D SLAM-3D mapping outdoor environments,” J. Field Robot. 24, 699–722 (2007).CrossRefGoogle Scholar
21.Rusinkiewicz, S. and Levoy, M., “Efficient Variants of the ICP Algorithm,” Proceedings of the Third International Conference on 3D Digital Imaging and Modeling, Quebec City, Canada (2001).Google Scholar
22.Se, S., Lowe, D. G. and Little, J. J., “Vision-based global localization and mapping for mobile robots,” IEEE Trans. Robot. 21, 3 (2005).CrossRefGoogle Scholar
23.Smith, R., Self, M. and Cheeseman, P., “Estimating Uncertain Spatial Relationships in Robotics,” In: Proceedings of the Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI'86) (New York, NY: Elsevier Science, 1986) pp. 267–288.Google Scholar
24.Storn, R. and Price, K., “Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces,” J. Glob. Optim. 11, 341–359 (Dec. 1997).CrossRefGoogle Scholar
25.Surmann, H., Nüchter, A., Lingemann, K. and Hertzberg, J., “6D SLAM – Preliminary Report on Closing The Loop in Six Dimensions,” Proceedings of the 5th IFAC Symposium on Intelligent Autonomous Vehicles (IAV'04), Lisbon (2004).Google Scholar
26.Surmann, H., Nüchter, A. and Hertzberg, J., “An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments,” Robot. Auton. Syst. 45 (3–4), 181–198 (2003).CrossRefGoogle Scholar
27.Thrun, S., Burgard, W. and Fox, D., “A Real-Time Algorithm for Mobile Robot Mapping with Applications to Multi-Robot and 3D Mapping,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'00), San Francisco, USA (2000).Google Scholar
28.Thrun, S., Montemerlo, M. and Aron, A., “Probabilistic Terrain Analysis for High-Speed Desert Driving,” In: Robotics: Science and Systems II, University of Pennsylvania, Philadelphia, Pennsylvania, USA (Aug. 16–19, 2006) (Cambridge, MA, USA).Google Scholar
29.Tomono, M., “A Scan Matching Method Using Euclidean Invariant Signature for Global Localization and Map Building,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'04), New Orleans, USA (2004).Google Scholar
30.Triebel, R., Pfaff, P. and Burgard, W., “Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'06), Beijing, China (2006).Google Scholar
31.Wulf, O., Arras, K. O., Christensen, H. I. and Wagner, B. A., “2D Mapping of Cluttered Indoor Environments by Means of 3D Perception,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'04), New Orleans, USA (Apr. 2004).Google Scholar
32.Zhang, Z., “Iterative point matching for registration of free-form curves and surfaces,” Int. J. Comput. Vis. 13, 119–152 (1994).CrossRefGoogle Scholar