The application discloses a facade obstacle detection method based on three-dimensional
laser point cloud data features. Before the cleaning
machine starts to move and clean, a
point cloud coordinate
system is established, and facade
point cloud information is acquired; next, abnormal points are filtered, the correct corresponding relationship between the point
cloud data and the real scene is acquired, and inherent parameters for coordinate
system correction are acquired. After the cleaning
machine starts to move, the rolling
brush position of cleaning is acquired as ROI, then the ROI is subjected to target detection and segmentation through a random sample
consensus algorithm, the point cloud of the obstacle on the facade is extracted, the point cloud of the same obstacle is merged through Euclidean clustering, and the position and size information of each obstacle are counted; finally, the obstacles are tracked through a Hungarian matching
algorithm and Kalman filtering. The application can realize automatic detection of obstacles of different facade scenes such as ordinary straight wall surfaces and tunnel walls, the whole process does not need manual intervention, and is used for automatic
obstacle avoidance of facade cleaning equipment.