In this paper, a dual position-sensitive detector-based vision measurement system camera is built instead of a traditional CCD camera. The 3D position information for the light point is calculated according to the 2D coordinate information of a certain light point in the space illuminated on the two position-sensitive detector (PSD) photosensitive surfaces, which is used for position detection of the spatial light point. In addition, the positioning model for 2D PSDs with different spot sizes in the Gaussian spot mode is derived by the mathematical model of Lucovsky's differential equation for a PSD. For the nonlinear distortion of the PSD, a nonlinear error calibration method using a particle swarm combined with a back propagation neural network is proposed to correct the errors in the measured values through the relationship between the input and output values, to obtain the predicted value that approximates the real coordinates. Then, by comparing the influence of different spot sizes on the positioning accuracy, we conclude that the smaller the spot formed by the convergence of the beam under the optical lens, the higher the positioning accuracy. We believe this conclusion can help improve the accuracy of PSD measurements. Finally, a red LED light spot is set up, and the 3D position measurement and error calibration of the light spot is done by dual PSD cameras, which better solves the position detection problem of a space light spot under close-range conditions because it is fast, reliable, and easy to implement. It also provides an effective method to detect the motion trajectory of a moving light spot in space.