Patent ID: 11971726
Assignee: WUHAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
Field: Control (Instruments)
Classification: CPC G | IPC G

Claim 0:
1. A method of constructing an indoor wall corner two-dimensional semantic map based on a robot platform,
the robot platform comprising a robot chassis, a master control computer, a lidar sensor, and a depth camera;
the robot chassis being provided with the master control computer, the lidar sensor, and the depth camera;
the master control computer being sequentially connected to the robot chassis, the lidar sensor, and the depth camera in a wired manner respectively;
the method of constructing an indoor wall corner two-dimensional semantic map comprising:
1) controlling, by the master control computer, the robot platform to move indoors, collecting, by the lidar sensor, a distance between an indoor object and the robot platform and a direction angle between the indoor object and the robot platform in real time and transmitting the distance and the direction angle to the master control computer, and obtaining, by the master control computer, an environment grid map and a real-time pose of the robot platform by processing the distance between the indoor object and the robot platform and the direction angle between the indoor object and the robot platform based on Gmapping algorithm;
2) Constructing a semantic segmentation data set as a training set on the master control computer and obtaining a predicted non-wall corner semantic label by inputting each non-wall corner sample image in the semantic segmentation data set into a DeepLab v2 network for prediction, and further constructing a DeepLab v2 network loss function in combination with the non-wall corner semantic label to obtain an optimized DeepLab v2 network through optimization training; and constructing a wall corner target detection data set as a training set and obtaining a predicted rectangular bounding box and an object type in the predicted rectangular bounding box by inputting each wall corner sample image in the wall corner target detection data set into a single shot multibox detector (SSD) network for prediction, and further constructing an SSD network loss function in combination with the prediction box and the object type in the wall corner marking box to obtain the optimized SSD network through optimization training;
3) Obtaining, by the master control computer, a view-angle color image of the robot platform by the depth camera, and inputting the view-angle color image into the optimized DeepLab v2 network for prediction to identify a semantic label of a non-wall corner in the view-angle color image; identifying the predicted rectangular bounding box of a wall corner and the object type in the predicted rectangular bounding box of the wall corner in the view-angle color image by passing the view-angle color image through the optimized SSD target detection network; and sequentially obtaining a three-dimensional point cloud coordinate of a non-wall corner and a three-dimensional point cloud coordinate of the wall corner by performing coordinate transformation for the semantic label of the non-wall corner in the view-angle color image, and the predicted rectangular bounding box of the wall corner and the object type in the predicted rectangular bounding box in the view-angle color image of the wall corner, and obtaining a filtered three-dimensional point cloud coordinate of the non-wall corner and a filtered three-dimensional point cloud coordinate of the wall corner by performing point cloud filtering for the three-dimensional point cloud coordinate of the non-wall corner and the three-dimensional point cloud coordinate of the wall corner respectively by using a filter based on a statistical method;
4) Obtaining, by the master control computer, a coordinate of a non-wall corner object in an environment grid map coordinate system and a coordinate of a wall corner object in the environment grid map coordinate system by performing point cloud coordinate transformation for the filtered three-dimensional point cloud coordinate of the non-wall corner and the filtered three-dimensional point cloud coordinate of the wall corner in combination with the real-time pose of the robot platform, and constructing an object grid map based on the coordinate of the non-wall corner object in the environment grid map coordinate system and the coordinate of the wall corner object in the environment grid map coordinate system; and
5) Obtaining a complete environment grid map and a complete object grid map by repeatedly performing 3-4) until the master control computer controls the robot platform to complete the traversal of the indoor environment, and further merging the complete environment grid map and the complete object grid map to obtain the indoor wall corner two-dimensional semantic map.