Vision-Based Real-Time Positioning and Autonomous Navigation System Using Mobile Robot in Indoor Environments

Research toward unmanned mobile robot navigation has gained significant importance in the last decade due to its potential applications in the location-based services industry. The increase in construction of large space indoor buildings has made difficulty for humans to operate within such environments. In this study, a mobile robot's indoor navigation algorithm is developed with vision cameras. Using two monocular cameras (one looking forward and one looking downward), the developed algorithms make use of the salient features of the environments to estimate rotational and translational motions for real-time positioning of the mobile robot. At the same time, an algorithm based on artificial landmark recognition is developed. The artificial landmark is shaped arrow based signboards with different colors representing different paths. These algorithms are integrated into a designed framework for mobile robot real-time positioning and autonomous navigation. Experiments are performed to validate the designed system using the mobile robot PIONEER P3-AT. The developed algorithm was able to detect and extract artificial landmark information up to 3 m distance for the mobile robot guidance. Experiment results show an average error of 0.167 m deviation from the ideal path, signified the good ability and performance of the development autonomous navigation algorithm.

[1]  Chenxi Sun,et al.  A vision-based guidance method for autonomous guided vehicles , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[2]  José Luis Gordillo,et al.  A new method to follow a path on indoor environments applied for mobile robotics , 2014, 11th IEEE International Conference on Control & Automation (ICCA).

[3]  Songmin Jia,et al.  Autonomous mobile robot guidance based on ground line mark , 2012, 2012 Proceedings of SICE Annual Conference (SICE).

[4]  Ji Zhang,et al.  Robust Monocular Visual Odometry for a Ground Vehicle in Undulating Terrain , 2012, FSR.

[5]  Arnaud Revel,et al.  Document image analysis by a mobile robot for autonomous indoor navigation , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[6]  Daniel Cremers,et al.  Accurate Figure Flying with a Quadrocopter Using Onboard Visual and Inertial Sensing , 2012 .

[7]  Bei Huang.  Floor Plan Based Indoor Vision Navigation Using Smart Device , 2013 .

[8]  P. K. Mishra,et al.  Understanding Color Models: A Review , 2012 .

[9]  Kehu Yang,et al.  Rotation estimation for mobile robot based on single-axis gyroscope and monocular camera , 2012, Int. J. Autom. Comput..

[10]  Dimitrios G. Kottas,et al.  Camera-IMU-based localization: Observability analysis and consistency improvement , 2014, Int. J. Robotics Res..

[11]  Ray Smith.  An Overview of the Tesseract OCR Engine , 2007 .

[12]  Tim D. Barfoot,et al.  Monocular Visual Teach and Repeat Aided by Local Ground Planarity , 2015, FSR.

[13]  Mamadou Diop,et al.  A Vision-Aided Motion Detection: 3-DOF Orientation Estimation Algorithm Using Mobile Robot , 2015 .

[14]  Naser El-Sheimy,et al.  Evaluation of Two WiFi Positioning Systems Based on Autonomous Crowdsourcing of Handheld Devices for Indoor Navigation , 2016, IEEE Transactions on Mobile Computing.

[15]  Tien Sze Lim,et al.  A Vision-based travelled Distance estimation Algorithm in an indoor Environment using a Mobile robot , 2014, J. Comput. Sci..

[16]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[17]  Yunhao Liu,et al.  VIRE: Active RFID-based Localization Using Virtual Reference Elimination , 2007, 2007 International Conference on Parallel Processing (ICPP 2007).

[18]  Tomás Krajník,et al.  Hybrid vision-based navigation for mobile robots in mixed indoor/outdoor environments , 2015, Pattern Recognit. Lett..

[19]  Anil A. Bharath,et al.  Appearance-based indoor localization: A comparison of patch descriptor performance , 2015, Pattern Recognit. Lett..

[20]  Aboelmagd Noureldin,et al.  Integrated Indoor Navigation System for Ground Vehicles With Automatic 3-D Alignment and Position Initialization , 2015, IEEE Transactions on Vehicular Technology.

[21]  M. Mercimek,et al.  Real object recognition using moment invariants , 2005 .