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Sensors 2016, 16(3), 311;

A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots

Department of Electrical and Computer Engineering, Automation and Systems Research Institute (ASRI), Seoul National University, Seoul 151-742, Korea
Inter-University Semiconductor Research Center (ISRC), Seoul National University, Seoul 151-742, Korea
Author to whom correspondence should be addressed.
Academic Editor: Yajing Shen
Received: 29 December 2015 / Revised: 3 February 2016 / Accepted: 17 February 2016 / Published: 1 March 2016
(This article belongs to the Special Issue Sensors for Robots)
Full-Text   |   PDF [3304 KB, uploaded 1 March 2016]   |  


This paper presents a monocular vision sensor-based obstacle detection algorithm for autonomous robots. Each individual image pixel at the bottom region of interest is labeled as belonging either to an obstacle or the floor. While conventional methods depend on point tracking for geometric cues for obstacle detection, the proposed algorithm uses the inverse perspective mapping (IPM) method. This method is much more advantageous when the camera is not high off the floor, which makes point tracking near the floor difficult. Markov random field-based obstacle segmentation is then performed using the IPM results and a floor appearance model. Next, the shortest distance between the robot and the obstacle is calculated. The algorithm is tested by applying it to 70 datasets, 20 of which include nonobstacle images where considerable changes in floor appearance occur. The obstacle segmentation accuracies and the distance estimation error are quantitatively analyzed. For obstacle datasets, the segmentation precision and the average distance estimation error of the proposed method are 81.4% and 1.6 cm, respectively, whereas those for a conventional method are 57.5% and 9.9 cm, respectively. For nonobstacle datasets, the proposed method gives 0.0% false positive rates, while the conventional method gives 17.6%. View Full-Text
Keywords: obstacle detection; monocular vision; segmentation obstacle detection; monocular vision; segmentation

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Lee, T.-J.; Yi, D.-H.; Cho, D.-I.“. A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots. Sensors 2016, 16, 311.

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