Precise location of indoor objects based on ROS and point cloud images

Considering that the map constructed by ROS SLAM can only describe the two-dimensional information of the environment, the three-dimensional point cloud image can only describe the independent three-dimensional information of the object, this paper combines the information of indoor two-dimensional map and object's three-dimensional point cloud image constructed by ROS SLAM's Gmapping algorithm, and proposes a composite coordinate positioning system. Firstly, different indoors are classified and one-dimensional coordinates are calibrated. Secondly, two-dimensional and three-dimensional coordinates are calibrated by the map constructed by Gmapping algorithm, and then external coordinates are formed by combining spatial information. Finally, three-dimensional coordinates of the collected object are obtained by affine operation based on external coordinates. Combined with one-dimensional coordinates, the object is combined with four dimensional coordinates. The whole positioning experiment data show that the average error of indoor position measurement is only 4.2 cm, and the positioning accuracy is 6.7% higher than that of the common ultrasonic and infrared positioning systems, 20% higher than that of the positioning system based on Bluetooth angle measurement, and 72% higher than that of the ultra-wideband positioning system. The object location error is small and the location is precise.