Machine Measuring Method for Norm-Position of Targets

摘要

Current methods for targets location in real-life scenarios are still based on the point-level location, which regard the target as a mass point and perform its position via 2D/3D coordinates. However, point-level location cannot describe the spatial structure or three-dimensional position of targets, which is not enough to provide location services that meet higher requirements in some special areas, such as scene regeneration and precision operation. In this paper, we propose a novel position measure called Norm-Position for this task. Norm-position describes the target location information by spatial structure and three-dimensional position, which no longer regards the target as a mass point. Norm-position measurement surveys the position of all surface points belonging to the same object. It does not only rely on traditional position measurement methods, but a novel machine measurement approach that combines key technologies such as high-precision positioning and timing based on Beidou navigation satellite, computer vision, lidar, artificial intelligence, 5G communications, big data, and cloud computing, to name a few. The presentation of norm-position measuring results is to calculate the three-dimensional coordinates, spatial structure and space occupation of multiple targets in the real scenes. In this paper, we also present a machine measuring method for norm-position and verify its effectiveness. Finally, we discuss the potentials of norm-position measurement in practical applications such as real-scene information management, unmanned precision operation and reverse control engineering.