Design of laser image recognition system based on high performance computing of spatiotemporal data

Due to the large scale and spatiotemporal dispersion of 3D (three-dimensional) point cloud data, current object recognition and semantic annotation methods still face issues of high computational complexity and slow data processing speed, resulting in data processing requiring much longer time than collection. This article studied the FPFH (Fast Point Feature Histograms) description method for local spatial features of point cloud data, achieving efficient extraction of local spatial features of point cloud data; This article investigated the robustness of point cloud data under different sample densities and noise environments. This article utilized the time delay of laser emission and reception signals to achieve distance measurement. Based on this, the measured object is continuously scanned to obtain the distance between the measured object and the measurement point. This article referred to the existing three-dimensional coordinate conversion method to obtain a two-dimensional lattice after three-dimensional position conversion. Based on the basic requirements of point cloud data processing, this article adopted a modular approach, with core functional modules such as input and output of point cloud data, visualization of point clouds, filtering of point clouds, extraction of key points of point clouds, feature extraction of point clouds, registration of point clouds, and data acquisition of point clouds. This can achieve efficient and convenient human-computer interaction for point clouds. This article used a laser image recognition system to screen potential objects, with a success rate of 85% and an accuracy rate of 82%. The laser image recognition system based on spatiotemporal data used in this article has high accuracy.