Localization of mobile robot using visual system

One of the main problems in mobile robotics is localization. This article aims to increase accuracy of localization of mobile robot using visual system. Mobile robot is taking images of environment containing artificial markers. Design of the markers was also one of the tasks. Markers can be used indoors and are placed on ceiling, where there is low number of other disturbing elements. They are designed to be able to split each marker into two parts—one is changeless and the second is changing with each marker. This provides an easy segmentation of all markers and then distinguishes between them easily as well. Localization is based on changes of markers angle and position in acquired images. During research of suitable algorithms, several approaches were tested to gain optimal results of localization considering time consumption and accuracy. The last part of this work deals with implementation of visual system and odometry fusion. That provides even more precise localization of the mobile robot.

[1]  Kenichi Mase,et al.  Improved Indoor Location Estimation Using Fluorescent Light Communication System with a Nine-Channel Receiver , 2010, IEICE Trans. Commun..

[2]  Majid Ahmadi,et al.  Robust indoor positioning using differential wi-fi access points , 2010, IEEE Transactions on Consumer Electronics.

[3]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[4]  Shree K. Nayar,et al.  Motion-based motion deblurring , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Kaspar Althoefer,et al.  A novel approach for Self-Localization based on Computer Vision and Artificial Marker Deposition , 2007, 2007 IEEE International Conference on Networking, Sensing and Control.

[6]  Jeong-Sik Park,et al.  Autonomous Position Estimation of a Mobile Node Based on Landmark and Localization Sensor , 2014, Int. J. Distributed Sens. Networks.

[7]  James L. Crowley,et al.  Position estimation for a mobile robot using vision and odometry , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[8]  Mohd Murtadha Mohamad,et al.  Wireless LAN/FM radio-based robust mobile indoor positioning: An initial outcome , 2014 .

[9]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[10]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[11]  Xu Zhong,et al.  Design and recognition of artificial landmarks for reliable indoor self-localization of mobile robots , 2017 .

[12]  Ravi Kumar Karri,et al.  Sensor Data Fusion Using Kalman Filter , 2018, 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C).

[13]  Sauro Longhi,et al.  Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots , 1999, IEEE Trans. Robotics Autom..

[14]  Andrew Blake,et al.  Motion Deblurring and Super-resolution from an Image Sequence , 1996, ECCV.

[15]  Min Wang,et al.  Target Tracking for Visual Servoing Systems Based on an Adaptive Kalman Filter , 2012 .

[16]  Peter K. Allen,et al.  Localization methods for a mobile robot in urban environments , 2004, IEEE Transactions on Robotics.

[17]  Andre M. Santana,et al.  Fusion of Odometry and Visual Datas to Localization a Mobile Robot Using Extended Kalman Filter , 2010 .