Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-13T06:44:41.620Z Has data issue: false hasContentIssue false
  • English
  • Français

Simultaneous landmark classification, localization and map building for an advanced sonar ring

Published online by Cambridge University Press:  01 May 2006

Saeid Fazli  and
Lindsay Kleeman
Saeid Fazli*
Affiliation:
ARC Centre for Perceptive and Intelligent Machines in Complex Environments (PIMCE), Intelligent Robotics Research Centre (IRRC), Department of Electrical and Computer Systems Engineering, Monash University, Australia. E-mail: [email protected]
Lindsay Kleeman
Affiliation:
ARC Centre for Perceptive and Intelligent Machines in Complex Environments (PIMCE), Intelligent Robotics Research Centre (IRRC), Department of Electrical and Computer Systems Engineering, Monash University, Australia. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

An autonomous mobile robot operating in an unknown indoor environment often needs to map the environment while localizing within the map. Feature-based world models including line and point features are widely used by researchers. This paper presents a novel delayed-classi-fication algorithm to categorize these features using a recently developed high-performance sonar ring within a simultaneous localization and map-building (SLAM) process. The sonar ring sensor accurately measures range and bearing to multiple targets at near real-time repetition rates of 11.5 Hz to 6 m range, and uses 24 simultaneously fired transmitters, 48 receivers and multiple echoes per receiver. The proposed algorithm is based on hypothesis generation and verification using the advanced sonar ring data and an extended Kalman filter (EKF) approach. It is capable of initiating new geometric features and classifying them within a short distance of travel of about 10 cm. For each new sonar reading not matching an existing feature, we initiate a pair of probational line and point features resulting from accurate range and bearing measurements. Later measurements are used to confirm or remove the probational features using EKF validation gates. The odometry error model of the filter allows for variations in effective wheel separation required by pneumatic robot tyres. The implementation of the novel classification and SLAM algorithm is discussed in this paper and experimental results using real sonar data are presented.

Keywords

SonarMap buildingSonar ringClassificationSLAMOdometry
Type
Article
Information
Robotica , Volume 25 , Issue 3 , May 2006 , pp. 283 - 296
Copyright
Copyright © Cambridge University Press 2006

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.Kleeman, L., “Fast and accurate sonar trackers using double pulse coding,” Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots with High Intelligence and Emotional Quotients, Kyongju, South Korea (Oct. 17–21, 1999) (IEEE, Piscataway, NJ) pp. 1185–1190.Google Scholar
2.Kuc, R. and Viard, V. B., “Physically based navigation strategy for sonar-guided vehicles,” Int. J. Robot. Res. 10, 75–87 1991.CrossRefGoogle Scholar
3.Murray, D. and Jennings, C., “Stereo vision based mapping and navigation for mobile robots,” Proceedings of the 1997 IEEE International Conference on Robotics and Automation (ICRA), Albuquerque, NM (Apr. 20–25, 1997) (IEEE, Piscataway, NJ) pp. 1694–1699.Google Scholar
4.Schroeter, C., Boehme, H.-J. and Gross, H.-M., “Robust map building for an autonomous robot using low-cost sensors,” Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics (SMC 2004), The Hague, The Netherlands (Oct. 10–13, 2004) (IEEE, New York) pp. 5398–5403.Google Scholar
5.Yata, T., Kleeman, L. and Yuta, S. I., “Fast-bearing measurement with a single ultrasonic transducer,” Int. J. Robot. Res. 17, 1202–1213 1998.CrossRefGoogle Scholar
6.Davison, A., Mobile Robot Navigation Using Active Vision D. Phil. Thesis (Oxford, UK: University of Oxford, 1998.Google Scholar
7.Diosi, A. and Kleeman, L., “Advanced sonar and laser range finder fusion for simultaneous localization and mapping,” Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sendai, Japan (Sep. 28–Oct. 2, 2004) (IEEE, New York) pp. 1854–1859.Google Scholar
8.Li, M.-H., Hong, B.-R. and Luo, R.-H.,“Simultaneous localization and map building for mobile robot,” Harbin Gongye Daxue Xuebao/J. Harbin Inst. Technol. 36, 874–876 2004.Google Scholar
9.Grossmann, A. and Poli, R., “Robust mobile robot localization from sparse and noisy proximity readings using Hough transform and probability grids,” Robot. Auton. Syst. 37, 1–18 2001.CrossRefGoogle Scholar
10.Gartshore, R., Aguado, A. and Galambos, C., “Incremental map building using an occupancy grid for an autonomous monocular robot,” Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision (ICARC), Singapore (Dec. 2–5, 2002) (Nanyang Technological University, Singapore) pp. 613–618.Google Scholar
11.Leonard, J. J., Durrant-Whyte, H. F. and Cox, I. J., “Dynamic map building for an autonomous mobile robot,” Int. J. Robot. Res. 11, 286–298 1992.CrossRefGoogle Scholar
12.Maksarov, D. and Durrant-Whyte, H. F., “Mobile vehicle navigation in unknown environments: A multiple hypothesis approach,” IEE Proc. Control Theory Appl. 142, 385–400 1995.CrossRefGoogle Scholar
13.Tsubouchi, T., “Nowadays trends in map generation for mobile robots,” Proceedings of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Osaka, Japan (Nov. 4–8, 1996) (IEEE, Piscataway, NJ) pp. 828–833.Google Scholar
14.Song, K.-T. and Chang, C. C., “Ultrasonic sensor data fusion for environment recognition,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Yokohama, Japan (Jul. 26–30, 1993) (IEEE, Piscataway, NJ) pp. 384–390.Google Scholar
15.Chong, K. S. and Kleeman, L., “Feature-based mapping in real, large scale environments using an ultrasonic array,” Int. J. Robot. Res. 18, 3–19 1999.Google Scholar
16.Kleeman, L., “Advanced sonar and odometry error modeling for simultaneous localization and map building,” Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV (Oct. 27–31, 2003) (IEEE, Piscataway, NJ) pp. 699–704.Google Scholar
17.Leonard, J. J. and Durrant-Whyte, H. F., “Simultaneous map building and localization for an autonomous mobile robot,” Proceedings of the IEEE International Workshop on Intelligent Robots and Systems, Osaka, Japan (Nov. 1991) pp. 1442–1447.Google Scholar
18.Wijk, O. and Christensen, H. I., “Triangulation-based fusion of sonar data with application in robot pose tracking,” IEEE Trans. Robot. Autom. 16, 740–752 2000.CrossRefGoogle Scholar
19.Rencken, W. D., “Autonomous sonar navigation in indoor, unknown and unstructured environments,” Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems, Munich, Germany (Sep. 12–16, 1994) (IEEE, Piscataway, NJ) pp. 431–438.Google Scholar
20.Zunino, G. and Christensen, H. I., “Simultaneous localization and mapping in domestic environments,” Proceedings of the International Conference on Multisensor Fusion and Integration for Intelligent Systems, Baden-Baden, Germany (Aug. 20–22, 2001) (IEEE, Piscataway, NJ) pp. 67–72.Google Scholar
21.Chong, K. S. and Kleeman, L., “Mobile-robot map building from an advanced sonar array and accurate odometry,” Int. J. Robot. Res. 18, 20–36 1999.Google Scholar
22.Heale, A. and Kleeman, L., “A real time DSP sonar echo processor,” Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems, Takamatsu, Japan (Oct. 31–Nov. 5, 2000) (IEEE, Piscataway, NJ) pp. 1261–1266.Google Scholar
23.Jeon, H. J. and Kim, B. K., “Study on world map building for mobile robots with tri-aural ultrasonic sensor system,” Proceedings of the 1995 IEEE International Conference on Robotics and Automation, Nagoya, Japan (May 21–27, 1995) (IEEE, Piscataway, NJ) pp. 2907–2912.CrossRefGoogle Scholar
24.Howell, J. and Donald, B. R., “Practical mobile robot self-localization,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), San Francisco, CA (Apr. 24–28, 2000) (IEEE, Piscataway, NJ) pp. 3485–3492.Google Scholar
25.Lorenzo, J. M. P., Vazquez-Martin, R., Nunez, P., Perez, E. J. and Sandoval, F., “A Hough-based method for concurrent mapping and localization in indoor environments,” Proceedings of the 2004 IEEE Conference on Robotics, Automation and Mechatronics, Singapore (Dec. 1–3, 2004) (IEEE, New York) pp. 840–845.Google Scholar
26.Luo, R.-H. and Hong, B.-R., “Simultaneous localization and mapping based on multisensor fusion,” Harbin Gongye Daxue Xuebao/J. Harbin Inst. Technol. 36, 566–569 2004.Google Scholar
27.Niblack, W. and Petkovic, D., “On improving the accuracy of the Hough transform,” Mach. Vis. Appl. 3, 87–106 1990.CrossRefGoogle Scholar
28.Yun, X., Latt, K. and Glennon, J. S., “Mobile robot localization using the Hough transform and neural networks,” Proceedings of the 1998 IEEE International Symposium on Intelligent Control, ISIC, Gaithersburg, MD (Sep. 14–17, 1998) (IEEE, Piscataway, NJ) pp. 393–400.Google Scholar
29.Fazli, S. and Kleeman, L., “Sensor design and signal processing for an advanced sonar ring,” Robotica, 24, 433–446 2006.CrossRefGoogle Scholar
30.Kleeman, L. and Kuc, R., “Mobile robot sonar for target localization and classification,” Int. J. Robot. Res. 14, 295–318 1995.CrossRefGoogle Scholar
31.Fazli, S. and Kleeman, L., “A low sample rate real time advanced sonar ring,” Proceedings of the 2004 Australasian Conference on Robotics and Automation, Canberra, Australia (Dec. 6–8 2004).Google Scholar
32.Fazli, S. and Kleeman, L., “A real time advanced sonar ring with simultaneous firing,” Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (Sep. 28–Oct. 2, 2004) pp. 1872–1877.Google Scholar
33.Bar-Shalom, Y., Rong, X. Li and Kirubarajan, T., Estimation with Applications to Tracking and Navigation, [electronic resource] 1st ed. (Wiley-Interscience, New York, 2001.Google Scholar
34.Bosse, M., Newman, P., Leonard, J. and Teller, S., “Simultaneous localization and map building in large-scale cyclic environments using the atlas framework,” Int. J. Robot. Res. 23, 1113–1139 2004.CrossRefGoogle Scholar
35.Kleeman, L. and Kuc, R., “Optimal sonar array for target localization and classification,” Proceedings of the 1994 IEEE International Conference on Robotics and Automation, San Diego, CA (May 8–13, 1994) (IEEE, Piscataway, NJ) pp. 3130–3135.CrossRefGoogle Scholar