Abstract
The quick and accurate understanding of the ambient environment, which is composed of road curbs, vehicles, pedestrians, etc., is critical for developing intelligent vehicles. The road elements included in this work are road curbs and dynamic road obstacles that directly affect the drivable area. A framework for the online modeling of the driving environment using a multi-beam LIDAR, i.e., a Velodyne HDL-64E LIDAR, which describes the 3D environment in the form of a point cloud, is reported in this article. First, ground segmentation is performed via multi-feature extraction of the raw data grabbed by the Velodyne LIDAR to satisfy the requirement of online environment modeling. Curbs and dynamic road obstacles are detected and tracked in different manners. Curves are fitted for curb points, and points are clustered into bundles whose form and kinematics parameters are calculated. The Kalman filter is used to track dynamic obstacles, whereas the snake model is employed for curbs. Results indicate that the proposed framework is robust under various environments and satisfies the requirements for online processing.
Highlights
Environment perception is a key research area as a source of information flow in developing unmanned ground vehicles (UGVs)
Unlike that of other sensors, the information provided by a camera is restricted to a certain view direction that covers a narrow field of view (FOV); distance information is lacking
The vehicle dynamic information obtained by Global Positioning System (GPS) and inertial navigation systems (INSs) is accurate and a road curb is a static obstacle; the local optimum value is sufficient to converge to the actual road curb in our experiment
Summary
Environment perception is a key research area as a source of information flow in developing unmanned ground vehicles (UGVs). Excellent results based on the vicinity information in the packet obtained from the Velodyne LIDAR were observed after the 2004 and 2005 Grand Challenges and the 2007 Urban Challenge held by the Defense Advanced Research Projects Agency to boost the development of UGVs. Von Hundelshausen et al [33] proposed an obstacle detection method based on the different values of points located within the same grid cell produced by a single beam. To our knowledge, tracking and detecting dynamic obstacles and road curbs are performed separately in previous works By contrast, these two processes are combined in the current work.
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