Method for detecting obstacle, electronic device, and storage medium

An electronic device, comprising:

• at least one processor, and
• a memory, communicatively connected to the at least one processor, wherein
• the memory stores an instruction executable by the at least one processor, and the instruction is performed by the at least one processor, to cause the at least one processor to perform an obstacle detection method:
• acquiring point cloud data collected by a lidar sensor during detection of an obstacle;
• converting the point cloud data into an obstacle feature map, wherein the obstacle feature map comprises a to-be-detected image feature, and the to-be-detected image feature comprises an obstacle feature, wherein the to-be-detected image feature is obtained by dividing the point cloud data according to pixels and obtaining the obstacle feature through post-processing manners including clustering;
• determining point cloud distribution data according to obstacle distribution information in the point cloud data;
• determining whether there is a corresponding obstacle in the point cloud data in a case that the point cloud data is not a null value;
• generating monitoring data based on a Gaussian distribution function and the point cloud distribution data when there is the corresponding obstacle in the point cloud data;
• performing feature extraction on the to-be-detected image feature in the obstacle feature map to obtain the obstacle feature, wherein a weight value in the obstacle feature map of the obstacle feature is increased during the feature extraction, and the feature extraction is monitored based on the monitoring data generated by point cloud distribution data corresponding to the point cloud data;
• and
• determining a target obstacle according to the obstacle feature.

A non-transitory computer readable storage medium, storing a computer instruction, wherein the computer instruction is used for a computer to perform an obstacle detection method:

• acquiring point cloud data collected by a lidar sensor during detection of an obstacle;
• converting the point cloud data into an obstacle feature map, wherein the obstacle feature map comprises a to-be-detected image feature, and the to-be-detected image feature comprises an obstacle feature, wherein the to-be-detected image feature is obtained by dividing the point cloud data according to pixels and obtaining the obstacle feature through post-processing manners including clustering;
• determining point cloud distribution data according to obstacle distribution information in the point cloud data;
• determining whether there is a corresponding obstacle in the point cloud data in a case that the point cloud data is not a null value;
• generating monitoring data based on a Gaussian distribution function and the point cloud distribution data when there is the corresponding obstacle in the point cloud data;
• performing feature extraction on the to-be-detected image feature in the obstacle feature map to obtain the obstacle feature, wherein a weight value in the obstacle feature map of the obstacle feature is increased during the feature extraction, and the feature extraction is monitored based on the monitoring data generated by point cloud distribution data corresponding to the point cloud data;
• and
• determining a target obstacle according to the obstacle feature.