• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.519-524
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• 2009

This paper describes a new sensor system for 3D environment perception using stereo structured infrared light sources and a camera. Environment and obstacle sensing is the key issue for mobile robot localization and navigation. Laser scanners and infrared scanners cover $180^{\circ}$ and are accurate but too expensive. Those sensors use rotating light beams so that the range measurements are constrained on a plane. 3D measurements are much more useful in many ways for obstacle detection, map building and localization. Stereo vision is very common way of getting the depth information of 3D environment. However, it requires that the correspondence should be clearly identified and it also heavily depends on the light condition of the environment. Instead of using stereo camera, monocular camera and two projected infrared light sources are used in order to reduce the effects of the ambient light while getting 3D depth map. Modeling of the projected light pattern enabled precise estimation of the range. Two successive captures of the image with left and right infrared light projection provide several benefits, which include wider area of depth measurement, higher spatial resolution and the visibility perception.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.108-115
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• 2011

This paper describes a new sensor system for mobile robot motion estimation using stereo infrared light sources and a camera. Visibility is being applied to robotic obstacle avoidance path planning and localization. Using simple visibility computation, the environment is partitioned into many visibility sectors. Based on the recognized edges, the sector a robot belongs to is identified and this greatly reduces the search area for localization. Geometric modeling of the vision system enables the estimation of the characteristic pixel position with respect to the robot movement. Finite difference analysis is used for incremental movement and the error sources are investigated. With two characteristic points in the image such as vertices, the robot position and orientation are successfully estimated.