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A monocular mobile robot reactive navigation approach based on the inverse perspective transformation

Published online by Cambridge University Press:  22 May 2012

Francisco Bonin-Font,
Antoni Burguera,
Alberto Ortiz and
Gabriel Oliver
Francisco Bonin-Font*
Affiliation:
Department of Mathematics and Computer Science, University of the Balearic Islands, Palma, Balearic Islands, Spain. E-mails: francisco.bonin@uib.es, antoni.burguera@uib.es, alberto.ortiz@uib.es and goliver@uib.es
Antoni Burguera
Affiliation:
Department of Mathematics and Computer Science, University of the Balearic Islands, Palma, Balearic Islands, Spain. E-mails: francisco.bonin@uib.es, antoni.burguera@uib.es, alberto.ortiz@uib.es and goliver@uib.es
Alberto Ortiz
Affiliation:
Department of Mathematics and Computer Science, University of the Balearic Islands, Palma, Balearic Islands, Spain. E-mails: francisco.bonin@uib.es, antoni.burguera@uib.es, alberto.ortiz@uib.es and goliver@uib.es
Gabriel Oliver
Affiliation:
Department of Mathematics and Computer Science, University of the Balearic Islands, Palma, Balearic Islands, Spain. E-mails: francisco.bonin@uib.es, antoni.burguera@uib.es, alberto.ortiz@uib.es and goliver@uib.es
*
*Corresponding author. E-mail: francisco.bonin@uib.es
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This paper presents an approach to visual obstacle avoidance and reactive robot navigation for outdoor and indoor environments. The obstacle detection algorithm includes an image feature tracking procedure followed by a feature classification process based on the IPT (Inverse Perspective Transformation). The classifier discriminates obstacle points from ground points. Obstacle features permit to draw out the obstacle boundaries which are used to construct a local and qualitative polar occupancy grid, analogously to a visual sonar. The navigation task is completed with a robocentric localization algorithm to compute the robot pose by means of an EKF (Extended Kalman Filter). The filter integrates the world coordinates of the ground points and the robot position in its state vector. The visual pose estimation process is intended to correct possible drifts on the dead-reckoning data provided by the proprioceptive robot sensors. The experiments, conducted indoors and outdoors, illustrate the range of scenarios where our proposal has proved to be useful, and show, both qualitatively and quantitatively, the benefits it provides.

Keywords

Mobile Autonomous RobotsVisual NavigationLocalizationFeature Traking

Information

Type
Articles
Information
Robotica , Volume 31 , Issue 2 , March 2013, pp. 225 - 249
Copyright
Copyright © Cambridge University Press 2012

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