Skip to main content Accessibility help
×
Home
Hostname: page-component-768ffcd9cc-jpcp9 Total loading time: 0.594 Render date: 2022-12-05T20:23:59.484Z Has data issue: true Feature Flags: { "useRatesEcommerce": false } hasContentIssue true

Article contents

Fast laser scan matching approach based on adaptive curvature estimation for mobile robots

Published online by Cambridge University Press:  01 May 2009

P. Núñez*
Affiliation:
Grupo de Telecomunicaciones, Dept. Tecnología de los Computadores y las Comunicaciones, Universidad de Extremadura, Cáceres, Spain.
R. Vázquez-Martín
Affiliation:
Grupo de Ingeniería de Sistemas Integrados, Dept. Tecnología Electrónica, Universidad de Málaga, 29071-Málaga, Spain.
A. Bandera
Affiliation:
Grupo de Ingeniería de Sistemas Integrados, Dept. Tecnología Electrónica, Universidad de Málaga, 29071-Málaga, Spain.
F. Sandoval
Affiliation:
Grupo de Ingeniería de Sistemas Integrados, Dept. Tecnología Electrónica, Universidad de Málaga, 29071-Málaga, Spain.
*
*Corresponding author. E-mail: pmnt@uma.es

Summary

This paper describes a complete laser-based approach for tracking the pose of a robot in a dynamic environment. The main novelty of this approach is that the matching between consecutively acquired scans is achieved using their associated curvature-based representations. The proposed scan matching algorithm consists of three stages. Firstly, the whole raw laser data is segmented into groups of consecutive range readings using a distance-based criterion and the curvature function for each group is computed. Then, this set of curvature functions is matched to the set of curvature functions associated to the previously acquired laser scan. Finally, characteristic points of pairwise curvature functions are matched and used to correctly obtain the best local alignment between consecutive scans. A closed form solution is employed for computing the optimal transformation and minimizing the robot pose shift error without iterations. Thus, the system is outstanding in terms of accuracy and computation time. The implemented algorithm is evaluated and compared to three state of the art scan matching approaches.

Type
Article
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Bandera, A., Urdiales, C., Arrebola, F. and Sandoval, F., “On-line Unsupervised Planar Shape Recognition based on Curvature Functions,” 24th Annual Conference of the IEEE Industrial Electronics Society (IECON'98), Aachen, Germany (1998), Vol. 3, pp. 12681272.CrossRefGoogle Scholar
2.Bengtsson, O. and Baerveldt, A. J., “Robot localization based in scan-matching estimating the covariance matrix for the IDC algorithm,” Robot. Autonom. Syst. 44 (4), 2940 (2003).CrossRefGoogle Scholar
3.Blanco, J. L., Fernández-Madrigal, J. A. and González, J. A., “Approach for Large-Scale Localization and Mapping: Hybrid Metric-Topological SLAM,” in IEEE International Conference on Robotics and Automation, Rome Italy (2007) pp. 20612067.Google Scholar
4.Borges, G. A. and Aldon, M., “Line extraction in 2-D range images for mobile robotics,” J. Intell. Robot. Syst. 40, 267297 (2004).CrossRefGoogle Scholar
5.Cox, I. J., “Blanche—an experiment in guidance and navigation of an autonomous robot vehicle,” IEEE Trans. Robot. Automation, 7 (2), 193204 (1991).CrossRefGoogle Scholar
6.Dissanayake, M., Newman, P., Clark, S., Durrant-Whyte, H. and Csorba, M., “A solution to the simultaneous localisation and map building problem,” IEEE Trans. Robot. Automation 17 (3), 229241 (2001).CrossRefGoogle Scholar
7.Gutmann, J. S. and Schlegel, C., “AMOS: Comparison of Scan Matching Approaches for Self-Localization in Indoor Environments,” in 1st Euromicro Workshop on Advanced Mobile Robots, Kaiserslautern, Germany (1996) pp. 61–67.Google Scholar
8.Hähnel, D., Fox, D., Burgard, W. and Thrun, S., “A highly efficient FastSLAM algorithm for generating cyclic maps of large-scale environments from raw laser range measurements,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, Nevada, USA. (2003) pp. 206211.Google Scholar
9.Lingemann, K., Nuchter, A., Hertzberg, J. and Surmann, H., “High-speed laser localization for mobile robots,” Robot. Autonom. Syst. 51 (4), 275296 (2005).CrossRefGoogle Scholar
10.Lu, F. and Milios, E. E., “Robot pose estimation in unknown environments by matching 2-D range scans,” Technical Report No. RBCV-TR-94-46, pp. 812. University of Toronto, Toronto (1994).Google Scholar
11.Montemerlo, M., FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem with Unknown Data Association Ph.D. Thesis (Carnegie Mellon University, 2003), Pittsburgh, Pensilvania, USA.Google Scholar
12.Nieto, J., Bailey, T. and Nebot, E., “Recursive scan-matching SLAM,” Robot. Autonom. Syst. 55, 3949 (2007).CrossRefGoogle Scholar
13.Núñez, P., Vázquez-Martín, R., del Toro, J. C., Bandera, A. and Sandoval, F., “Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation,” Robot. Autonom. Syst. 56 (3), 247264 (2008).CrossRefGoogle Scholar
14.Pfister, S., Algorithms for Mobile Robot Localization and Mapping, Incorporating Detailed Noise Modeling and Multi-Scale Feature Extraction Ph.D. Thesis (California Institute of Technology, 2006), Pasadena, California, USA.Google Scholar
15.Wang, C. C., Thorpe, C. and Thrun, S., “Online simultaneous localization and mapping with detection and tracking of moving objects: Theory and results from a ground vehicle in crowded urban areas,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Taipei, Taiwan (2003) pp. 842849.Google Scholar
16.Weiss, G. and Puttkamer, E., “A map based on laser scans without geometric interpretation,” Intell. Autonom. Syst. 4 403407 (1995).Google Scholar
22
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Fast laser scan matching approach based on adaptive curvature estimation for mobile robots
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Fast laser scan matching approach based on adaptive curvature estimation for mobile robots
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Fast laser scan matching approach based on adaptive curvature estimation for mobile robots
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *