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.
Highlights
Generating of a high-precision map grows up with development of autonomous driving vehicles
We propose a classification method of point cloud data from a Lidar mounted on a vehicle using a machine learning algorithm
Our approach for a classification of 3D point clouds is based on machine learning methods
Summary
Generating of a high-precision map grows up with development of autonomous driving vehicles. Lanes and signs are distinguished in the high-precision map For these reasons, it has been used to implement a localization of the unmanned vehicles and recognize the road environments. One of source data is point cloud data from a Lidar for creating the high-precision map. Serna and Marcotegui (2014) suggested a detection, segmentation and classification methodology of Lidar data in urban environments using mathematical morphology and supervised learning. It was important to define optimal neighbours of a point for an accurate feature extraction in the study Classifiers such as decision trees via bootstrap were combined for supervised classification of a huge point cloud data. We propose a classification method of point cloud data from a Lidar mounted on a vehicle using a machine learning algorithm. Each point is classified to road surfaces or objects based on the features using SVM
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