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도심지역 적용을 위한 uv-disparity 기반 장애물 검출 개선 방안
서재규(Jae Kyu Suhr),정호기(Ho Gi Jung) 한국자동차공학회 2011 한국자동차공학회 학술대회 및 전시회 Vol.2011 No.11
Obstacle detection based on v-disparity is one of the popular methods for detecting obstacles using a stereo camera. However, when applying this method to urban environments, v-disparity can severely be affected by buildings beside the roads. Therefore, this paper proposes a method to remove building surfaces from uv-disparity for enhancing the performance of vdisparity-based obstacle detection in urban environments. The proposed method first finds pixels consisting road surface and detects lanes to estimate the location of vanishing point. Then, u-disparity is divided into left and right parts according to the location of the vanishing point. Finally, building surfaces are detected and eliminated by estimating lines in each part of udisparity by using Hough transformation. Experimental results reveal that the uv-disparity with the proposed method more clearly shows obstacle regions compared to the uv-disparity without it.
서재규(Jae Kyu Suhr),정호기(Ho Gi Jung) 한국자동차공학회 2013 한국자동차공학회 부문종합 학술대회 Vol.2013 No.5
Almost all the previous automatic parking systems update relative position between ego-vehicle and target parking slot by utilizing only in-vehicle motion sensor-based odometry. However, performance of these systems can be degraded due to cumulative errors of in-vehicle motion sensor-based odometry. To overcome this drawback, this paper proposes a method that continuously tracks target parking slot position by fusing around view monitor (AVM) images and in-vehicle motion sensor-based odometry. In experiments, the proposed method successfully tracks various types of parking slot markings in spite of severe occlusions caused by ego-vehicle.
Piece-wise Linear 함수 기반 스테레오 영상에서의 도로면 추정 개선 방법
서재규(Jae Kyu Suhr),정호기(Ho Gi Jung) 한국자동차공학회 2012 한국자동차공학회 학술대회 및 전시회 Vol.2012 No.11
This paper proposes a novel road surface estimation method using a piece-wise linear function and RANSAC framework. The proposed method achieves robustness against 3D points on obstacle surfaces by sampling 3D points expected to compose road surface, and it also makes the estimation procedure insensitive to stereo matching errors on textureless road regions by sequentially calculating a piece-wise linear function using a RANSAC-based robust line estimator with adaptively chosen road interval and slope angle parameters. Experimental results show that the proposed method successfully estimates road surfaces in various real world situations including complex road surface shape and severe stereo matching error.