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Wang-Heon Lee

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3 papers

1 author row

IROS Conference 2003 Conference Paper

Automatic edge detection method for the mobile robot application

Wang-Heon Lee
Dongsu Kim
In-So Kweon

This paper proposes a new edge detection method using a 3/spl times/3 ideal binary pattern and lookup table (LUT) for the mobile robot localization without any parameter adjustments. We take the mean of the pixels within the 3/spl times/3 block as a threshold by which the pixels are divided into two groups. The edge magnitude and orientation are calculated by taking the difference of average intensities of the two groups and by searching directional code in the LUT, respectively. And also the input image is not only partitioned into multiple groups according to their intensity similarities by the histogram, but also the threshold of each group is determined by fuzzy reasoning automatically. Finally, the edges are determined through non-maximum suppression using edge confidence measure and edge linking. Applying this edge detection method to the mobile robot localization using projective invariance of the cross ratio, we demonstrate the robustness of the proposed method to the illumination changes in a corridor environment.

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ICRA Conference 2002 Conference Paper

Color Landmark Based Self-Localization for Indoor Mobile Robots

Gi-jeong Jang
Sungho Kim 0003
Wang-Heon Lee
In-So Kweon

We present a simple artificial landmark model and a robust tracking algorithm for the navigation of indoor mobile robots. The landmark model is designed to have a three-dimensional structure consisting of a multi-colored planar pattern. A stochastic algorithm based on Condensation [1] tracks the landmark model robustly using the color distribution of the pattern. A new self-localization algorithm computes the location of robot with the tracked single landmark. Experimental results show that the proposed landmark model is eflective. Through extensive navigation experiments in a cluttered indoor environment, we demonstrate the feasibility of the single view based self-localization in real-time.

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IROS Conference 1997 Conference Paper

Obstacle detection and self-localization without camera calibration using projective invariants

Kyoung-Sig Roh
Wang-Heon Lee
In-So Kweon

In this paper, we propose two new vision-based methods for indoor mobile robot navigation. One is a self-localization algorithm using projective invariant and the other is a method for obstacle detection by simple image difference and relative positioning. For a geometric model of corridor environment, we use natural features formed by floor, walls, and door frames. Using the cross-ratios of the features can be effective and robust in building and updating model-base, and image matching. We predefine a risk zone without obstacles for a robot, and store the image of the risk zone, which will be used to detect obstacles inside the zone by comparing the stored image with the current image of a new risk zone. The position of the robot and obstacles are determined by relative positioning. The robustness and feasibility of our algorithms have been demonstrated through experiments in corridor environments using the KASIRI-II indoor mobile robot.

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