Visual Localization for Mobile Robots Based on Composite Map
Hung-Hsiu Yu*, Hsiang-Wen Hsieh*, Yu-Kuen Tasi*,
Zhi-Hung Ou**, Yea-Shuan Huang**, and Toshio Fukuda***
*Intelligent Robotics Division, Mechanical and System Laboratory, Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 310, Taiwan
**Department of Computer Science and Information Engineering, Chung-Hua University, 707, Sec. 2, WuFu Rd., Hsinchu 30012, Taiwan
***Department of Micro-Nano Systems Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
In this paper, we propose a novel mobile robot visual localization method consisting of two processing stages: map construction and visual localization. In the map construction stage, both laser range finder and camera are used to construct a composite map. Depth data are collected from laser range finder while distinct features of salient feature points are gathered from camera provided images. In the visual localization stage, only camera is used and the robot system detects feature points from camera provided images, computes features of the detected feature points, matches them with the features recorded in previously constructed composite map, and decides location of the robot. Using this method, a robot can locate its own position effectively without expensive laser range finder so that greater acceptance can be expected due to affordability. With the proposedmethod, several experiments have been performed. The matching accuracy of proposed feature extraction achieves 97.79%, compared with 92.96% of SURF. Experiment results show that our method not only reduces hardware cost of robot localization, but also offers high accuracy.
Zhi-Hung Ou, Yea-Shuan Huang, and Toshio Fukuda, “Visual Localization for Mobile Robots Based on Composite Map,” J. Robot. Mechatron., Vol.25, No.1, pp. 25-37, 2013.
-  S. Thrun, W. Burgard, and D. Fox, “Probabilistic robotics,” MIT Press, 2005.
-  D. Fox, W. Burgard, S. Thrun, and A. B. Cremers, “Position Estimation for Mobile Robots in Dynamic Environments,” Proc. of the National Conf. on Artificial Intelligence, 1998.
-  W. Burgard, D. Fox, D. Hennig, and T. Schmidt, “Position tracking with position probability grids,” Proc. of the First Euromicro Workshop on Advanced Mobile Robot, pp. 2-9, 1996.
-  T. Ogino, M. Tomono, T. Akimoto, and A. Matsumoto, “Human Following by an Omnidirectional Mobile Robot Using Maps Built from Laser Range-Finder Measurement,” J. of Robotics andMechatronics, Vol.22. No.1, pp. 28-35, 2010.
-  C. Rohrig, D. Hess, C. Kirsch, and F. Kunemund, “Localization of an omnidirectional transport robot using IEEE 802.15.4a ranging and laser range finder,” Int. Conf. on Intelligent Robots and Systems, pp. 3798-3803, 2010.
-  H. Yoshitaka, K. Hirohiko, O. Akihisa, and Y. Shin’ichi, “Mobile Robot Localization and Mapping by Scan Matching using Laser Reflection Intensity of the SOKUIKI Sensor,” Conf. on IEEE Industrial Electronics, pp. 3018-3023, 2006.
-  X. Zhou, Y. K. Ho, C. S. Chua, and Y. Zou, “The localization of mobile robot based on laser scanner,” Conf. on Electrical and Computer Engineering, Vol.2, pp. 841-845, 2000.
-  I. Nagai and Y. Tanaka, “Mobile Robot with Floor Tracking Device for Localization and Control,” J. of Robotics and Mechatronics, Vol.19, No.1, pp. 34-41, 2007.
-  H. Yaguchi, N. Zaoputra, N. Hatao, K. Yamazaki, K. Okada, and M. Inaba, “View-Based Localization Using Head-Mounted Multi Sensors Information,” J. of Robotics and Mechatronics, Vol.21, No.3, pp. 376-383, 2009.
-  E. L. Akers and A. Agah, “Topological Localization Using Appearance-Based Recognition,” J. of Automation Mobile Robotics Intelligent Systems, Vol.41, pp. 68-84, 2010.
-  A. J. Davison and D. W. Murray, “Simultaneous localization and map-building using active vision,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.24, No.7, pp. 865-880, 2002.
-  J. Diebel, K. Reutersward, S. Thrun, J. Davis, and R. Gupta, “Simultaneous localization and mapping with active stereo vision,” Int. Conf. on Intelligent Robots and Systems IROS IEEE, Vol.4, pp. 3436-3443, 2004.
-  J. Porta, J. Verbeek, and B. Krose, “Active appearance-based robot localization using stereo vision,” Autonomous Robots, Vol.18, No.1, pp. 59-80, 2005.
-  A. Davison, I. Reid, N.Molton, and O. Stasse, “MonoSLAM: Real-Time Single Camera SLAM,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.29, No.6, pp. 1052-1067, 2007.
-  C.-H. Chen and Y.-P. Chan, “SIFT-based monocular SLAM with inverse depth parameterization for robot localization,” IEEE Workshop on Advanced Robotics and Its Social Impacts, pp. 1-6, 2007.
-  S. Se, D. Lowe, and J. Little, “Vision-based mobile robot localization and mapping using scale-invariant features,” Proc. of IEEE Int. Conf. on Robotics and Automation, Vol.2, pp. 2051-2058, 2001.
-  A. Georgiev and P. Allen, “Vision for mobile robot localization in urban environments,” Int. Conf. on Intelligent Robots and Systems, Vol.1, pp. 472-477, 2002.
-  B. Ristic, S. Arulampalam, and N. Gordon, “Beyond the Kalman filter: particle filters for tracking applications,” Artech House, 2004.
-  M. Nicoli, C. Morelli, and V. Rampa, “A Jump Markov Particle Filter for Localization of Moving Terminals in Multipath Indoor Scenarios,” IEEE Trans. on Signal Processing, Vol.56, No.8, pp. 3801-3809, 2008.
-  M. Yokozuka, Y. Suzuki, T. Takei, N. Hashimoto, and O. Matsumoto, “Auxiliary Particle Filter Localization for Intelligent Wheelchair Systems in Urban Environments,” J. of Robotics and Mechatronics, Vol.22, No.6, pp. 758-766, 2010.
-  A. Sakai, T. Saitoh, and Y. Kuroda, “Robust Landmark Estimation and Unscented Particle Sampling for SLAM in Dynamic Outdoor Environment,” J. of Robotics and Mechatronics, Vol.22, No.2, pp. 140-149, 2010.
-  C. Harris and M. Stephens, “A Combined Corner and Edge Detection,” Proc. of The Fourth Alvey Vision Conf., pp. 147-151, 1988.
-  Y. Han, J. Yin, and J. Li, “Human Face Feature Extraction and Recognition Base on SIFT,” Int. Symposium on Computer Science and Computational Technology, Vol.1, pp. 719-722, 2008.
-  S. M. Smith and J. M. Brady, “SUSAN – A New Approach to Low Level Image Processing,” Int. J. of Computer Vision, Vol.23, pp. 45-78, 1995.
-  E. Rosten and T. Drummond, “Machine learning for high-speed corner detection,” European Conf. on Computer Vision, pp. 430-443, 2006.
-  J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol.1, pp. 81-106, 1986.
-  S. Aoyagi, N. Hattori, A. Kohama, S. Komai, M. Suzuki, M. Takano, and E. Fukui, “Object Detection and Recognition Using Template Matching with SIFT Features Assisted by Invisible Floor Marks,” J. of Robotics and Mechatronics, Vol.21, No.6, pp. 689-697, 2009.
-  H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding, Vol.110, No.3, pp. 346-359, 2008.
-  Z. Zhang, “Flexible camera calibration by viewing a plane from unknown orientations,” Proc. of the Seventh IEEE Int. Conf. on Computer Vision, Vol.1, pp. 666-673, 1999.
-  Q. Zhang and R. Pless, “Extrinsic calibration of a camera and laser range finder (improves camera calibration),” Proc. of Int. Conf. on Intelligent Robots and Systems, Vol.3, pp. 2301-2306, 2004.
-  T. Ojala, M. Pietikainen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition, Vol.29, No.1, pp. 51-59, 1996.
-  T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.24, No.7, pp. 971-987, 2002.
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