Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 251-254, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-251-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
09 Jun 2016
CLASSIFICATION OF LIDAR DATA FOR GENERATING A HIGH-PRECISION ROADWAY MAP
J. Jeong and I. Lee Dept of Geoinformatics, University of Seoul, 163, Seoulsiripdae-ro, Dongdaemun-gu Seoul, Korea
Keywords: Lidar, Feature extraction, Machine learning, Classification, Mobile mapping Abstract. Generating of a highly precise map grows up with development of autonomous driving vehicles. The highly precise map includes a precision of centimetres level unlike an existing commercial map with the precision of meters level. It is important to understand road environments and make a decision for autonomous driving since a robust localization is one of the critical challenges for the autonomous driving car. The one of source data is from a Lidar because it provides highly dense point cloud data with three dimensional position, intensities and ranges from the sensor to target. In this paper, we focus on how to segment point cloud data from a Lidar on a vehicle and classify objects on the road for the highly precise map. In particular, we propose the combination with a feature descriptor and a classification algorithm in machine learning. Objects can be distinguish by geometrical features based on a surface normal of each point. To achieve correct classification using limited point cloud data sets, a Support Vector Machine algorithm in machine learning are used. Final step is to evaluate accuracies of obtained results by comparing them to reference data The results show sufficient accuracy and it will be utilized to generate a highly precise road map.
Conference paper (PDF, 1004 KB)


Citation: Jeong, J. and Lee, I.: CLASSIFICATION OF LIDAR DATA FOR GENERATING A HIGH-PRECISION ROADWAY MAP, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 251-254, https://doi.org/10.5194/isprs-archives-XLI-B3-251-2016, 2016.

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