Through the Connected-Component Labelling algorithm, these occupied grid cells are then clustered and all the outliers with only a few grid cells are eliminated. Thereafter this occupancy grid map is transformed into a binary grid map, where the grid cells, with an obstacle present, are defined as occupied. According to the movement of the test vehicle, the a posteriori occupancy probability after several measurement cycles is computed to build the occupancy grid map. The reflection amplitudes of all data points are compensated, normalized, and then converted to a detection probability value that is based on a predefined radar sensor model. To model the static environment with these detection points, a novel approach of occupancy grid mapping is developed in this paper. High-resolution radar sensors have the capability to perceive the surroundings around the vehicle very exactly by detecting thousands of reflection points per measurement cycle.
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