Obstacle clustering method and obstacle clustering device

Abstract

The invention relates to an obstacle clustering method and an obstacle clustering device. The method comprises steps: three-dimensional point cloud data are acquired, and coordinates of a mapping point on a vehicle body coordinate system are determined; the mapping point is projected to a grid map; an obstacle point is recognized according to the mapping point coordinates and the grid map; the obstacle point is clustered to acquire K obstacle clustering clusters and K clustering centers; the similarly between the obstacle point and each clustering center is calculated, and the obstacle point is divided to an obstacle clustering cluster corresponding to a clustering center with the highest similarity; the clustering center is updated; whether each clustering center meets a convergence condition is judged; and when a clustering center not meeting the convergence condition exists, the step of calculating the similarly between the obstacle point and each clustering center and dividing the obstacle point to an obstacle clustering cluster corresponding to a clustering center with the highest similarity is returned until all clustering centers meet the convergence condition. Thus, obstacle clustering can be realized accurately and reliably, and the obstacle recognition rate can be improved.

Description

technical field [0001] The present disclosure relates to the technical field of intelligent transportation, and in particular, to an obstacle clustering method and device. Background technique [0002] The environment perception technology of the autonomous vehicle in the outdoor environment is the key technology for its autonomous navigation, and the detection of obstacles is essential for correct and safe navigation. In the autonomous vehicle system, sensors such as binocular stereo cameras and lidar are commonly used for obstacle detection. Compared with binocular stereo cameras, lidar is better in accuracy and detection range. However, due to the limited scanning range of 3D lidar and the inability to detect objects of certain materials, the detection of obstacles by lidar in a relatively complex environment will be biased. Therefore, how to accurately identify the obstacle information in the surrounding environment based on the data detected by the lidar has extensive...

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Problems solved by technology

[0003] The purpose of this disclosure is to provide an obstacle clustering method and device for the problem of deviation in the detection of obstacles by lidar in a relatively complex environment in the prior art

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