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Distance Estimation Method using Enhanced Adaptive Fuzzy Strong Tracking Kalman Filter Based on Stereo Vision  

Lim, Young-Chul (Daegu Gyeongbuk Institute of Science & Technology)
Lee, Chung-Hee (Daegu Gyeongbuk Institute of Science & Technology)
Kwon, Soon (Daegu Gyeongbuk Institute of Science & Technology)
Lee, Jong-Hoon (Daegu Gyeongbuk Institute of Science & Technology)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.45, no.6, 2008 , pp. 108-116 More about this Journal
Abstract
In this paper, we propose an algorithm that can estimate the distance using disparity based on stereo vision system, even though the obstacle is located in long ranges as well as short ranges. We use sub-pixel interpolation to minimize quantization errors which deteriorate the distance accuracy when calculating the distance with integer disparity, and also we use enhanced adaptive fuzzy strong tracking Kalman filter(EAFSTKF) to improve the distance accuracy and track the path optimally. The proposed method can solve the divergence problem caused by nonlinear dynamics such as various vehicle movements in the conventional Kalman filter(CKF), and also enhance the distance accuracy and reliability. Our simulation results show that the performance of our method improves by about 13.5% compared to other methods in point of root mean square error rate(RMSER).
Keywords
stereo vision; distance estimation; sub-pixel interpolation; Kalman filter; fuzzy system;
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