Solving the Indoor SLAM Problem for a Low-Cost Robot using Sensor Data Fusion and Autonomous Feature-based Exploration

Luciano Buonocore, Cairo Lúcio Nascimento Júnior, Areolino de Almeida Neto

Abstract

This article is concerned with the solution of the SLAM (Simultaneous Localization And Mapping) problem in an indoor environment using a low-cost mobile robot that autonomously explores the environment. The robot was constructed with a distance measurement subsystem composed of three types of sensors: a wireless webcam with a laser pointer (a visual sensor), two infrared sensors and an ultrasonic TOF (time-of-flight) sensor. Firstly, an algorithm that requires a small computational load is used to fuse the noisy raw data acquired by these sensors and generate the environment features. These features are then used by a particle filter to solve the SLAM problem. An autonomous feature-based exploration algorithm was implemented and is also presented. The results obtained in the experiments carried out in two small indoor environments show that the estimated environment map generated when the robot uses the autonomous exploration algorithm is very similar to the one generated when the robot poses were manually chosen.

References

  1. Aguirre, E., González, A., 2002. Integrating fuzzy topological maps and fuzzy geometric maps for behavior-based robots. International Journal of Intelligent Systems, no. 17, pp. 333-368.
  2. Buonocore, L., Nascimento Jr., C. L., Almeida Neto, A., 2010. Sistema de baixo custo para medição de distância em duas dimensões usando câmera IP sem fio. 18º. Congresso Brasileiro de Automática, Bonito, MS, Brazil, pp. 2239 - 2246.
  3. Castellanos, J. A., Martínez, J. M., Neira, J., Tardós, J. D., 1998. Simultaneous map building and localization for mobile robots: a multisensor fusion approach. International Conference on Robotics & Automation, vol. 2, pp. 1244-1249.
  4. Huge, D-W., 2001. Multi sensor data fusion. [e-book] Available at: < http://www.acfr.usyd.edu.au/pdfs/ trai ning/multiSensorDataFusion/dataFusionNotes.pdf> [Acessed 12 September 2009].
  5. Huge, D-W., Bailey, T., 2006. Simultaneous localization and mapping: Part I. Tutorial. Robotics & Automation Magazine, vol. 13, no. 3, pp. 99-108.
  6. Ivanjko, E., Petrovic, I., Brezak, M., 2009. Experimental comparison of sonar based occupancy grid mapping methods. AUTOMATIKA: Journal for Control, Measurement, Electronics, Computing and Communications, vol. 50, no. 1-2, pp. 65-79.
  7. Milisavljevic, N., Bloch, I., Acheroy, M., 2008. Multisensor data fusion based on belief functions and possibility theory: close range antipersonnel mine detection and remote sensing mined area reduction. ITech Education and Publishing, pp. 95-120.
  8. Nguyen, H. G., Blackburn, M. R. (1995). A simple method for range finding via laser triangulation, Technical Document 2734, Naval Command, Control and Ocean Surveillance Center RDT&E Division, San Diego, CA.
  9. Newman, P., Bosse, M., Leonard, J, 2003. Autonomous feature-based exploration. International Conference on Robotics and Automation, vol. 1, pp. 1234-1240.
  10. Pandey, A. K., Krishna, K. M., Nath, M., 2007. Feature based grid maps for sonar based safe-mapping. International Joint Conferences on Artificial Intelligence, pp. 2172-2177.
  11. Thrun, S. B., 1993. Exploration and model building in mobile robot domains. International Neural Networks, vol. 1, pp. 175-180.
  12. Thrun, S. B., Burgard, W., Fox, D., 2005. Probabilistic Robotics. Cambridge, USA: MIT Press.
  13. Vazquez, J., Malcolm, C., 2005. Fusion of triangulated sonar plus infrared sensing for localization and mapping. International Conference on Control and Automation, vol. 2, pp. 1097-1102.
  14. Visser, A., 1999. Chapter 9 - Sensor Data Fusion. [pdf] Available at: < http://www.science.uva.nl/arnoud/edu cation/OOAS/fwi/Chap9.pdf> [Acessed 12 September 2009].
  15. Yap Jr., T. N., Shelton, C. R., 2009. SLAM in large indoor environments with low-cost, noisy and sparse sonars. IEEE International Conference on Robotics and Automation, Kobe, Japan, pp. 1395 - 1401.
  16. Zuliani, M., 2009. RANSAC for Dummies-Draft. [pdf] Available at: < http://vision.ece.ucsb.edu/zuliani/Res earch/RANSAC/docs/RANSAC4Dummies.pdf > [Accessed 15 August 2009].
Download


Paper Citation


in Harvard Style

Buonocore L., Lúcio Nascimento Júnior C. and de Almeida Neto A. (2012). Solving the Indoor SLAM Problem for a Low-Cost Robot using Sensor Data Fusion and Autonomous Feature-based Exploration . In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-8565-22-8, pages 407-414. DOI: 10.5220/0004000504070414


in Bibtex Style

@conference{icinco12,
author={Luciano Buonocore and Cairo Lúcio Nascimento Júnior and Areolino de Almeida Neto},
title={Solving the Indoor SLAM Problem for a Low-Cost Robot using Sensor Data Fusion and Autonomous Feature-based Exploration},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2012},
pages={407-414},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004000504070414},
isbn={978-989-8565-22-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Solving the Indoor SLAM Problem for a Low-Cost Robot using Sensor Data Fusion and Autonomous Feature-based Exploration
SN - 978-989-8565-22-8
AU - Buonocore L.
AU - Lúcio Nascimento Júnior C.
AU - de Almeida Neto A.
PY - 2012
SP - 407
EP - 414
DO - 10.5220/0004000504070414