Laser radar is widely used in terrain mapping, unmanned driving and other fields because of its characteristics of accurate ranging, high sensitivity and all-weather work. However, more ground point clouds have a negative impact on the accuracy and speed of salient object detection. How to use laser point cloud filtering technology to efficiently extract ground points and non-ground point areas has become the key to improve the calculation speed and reduce the amount of computation. Therefore, this paper takes the SemanticKITTI dataset as the research object and combines theory with experiment to compare the effectiveness of the three-laser point cloud filtering algorithms of current mainstream morphology, CSF and PTD. The research work has guiding significance for scientifically selecting effective laser filtering algorithms.
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