Please use this identifier to cite or link to this item:
|Title:||Robust curb detection with fusion of 3d-Lidar and camera data||Authors:||Tan, J.
|Issue Date:||21-May-2014||Citation:||Tan, J., Li, J., An, X., He, H. (2014-05-21). Robust curb detection with fusion of 3d-Lidar and camera data. Sensors (Switzerland) 14 (5) : 9046-9073. ScholarBank@NUS Repository. https://doi.org/10.3390/s140509046||Abstract:||Curb detection is an essential component of Autonomous Land Vehicles (ALV), especially important for safe driving in urban environments. In this paper, we propose a fusion-based curb detection method through exploiting 3D-Lidar and camera data. More specifically, we first fuse the sparse 3D-Lidar points and high-resolution camera images together to recover a dense depth image of the captured scene. Based on the recovered dense depth image, we propose a filter-based method to estimate the normal direction within the image. Then, by using the multi-scale normal patterns based on the curb's geometric property, curb point features fitting the patterns are detected in the normal image row by row. After that, we construct a Markov Chain to model the consistency of curb points which utilizes the continuous property of the curb, and thus the optimal curb path which links the curb points together can be efficiently estimated by dynamic programming. Finally, we perform post-processing operations to filter the outliers, parameterize the curbs and give the confidence scores on the detected curbs. Extensive evaluations clearly show that our proposed method can detect curbs with strong robustness at real-time speed for both static and dynamic scenes. © 2014 by the authors; licensee MDPI, Basel, Switzerland.||Source Title:||Sensors (Switzerland)||URI:||http://scholarbank.nus.edu.sg/handle/10635/125101||ISSN:||14248220||DOI:||10.3390/s140509046|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 26, 2021
WEB OF SCIENCETM
checked on Oct 18, 2021
checked on Oct 14, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.