Camera Arrangement using Geometric Optimization to Minimize Localization Error in Stereo-vision Systems

Stereo-machine vision can be used as a space sampling technique, and the cameras parameters and configuration can effectively change the number of samples in each volume of space called the space sampling density (SSD). Using the concept of voxels, in this work, we presents a method to optimize the geometric configuration of the cameras in order to maximize SSD, which means minimizing the voxel volume and reducing the uncertainty in localizing an object in a 3D space. Each pixel’s field of view (FOV) is considered as a skew pyramid. The uncertainty region will be created from the intersection of two pyramids associated with any of the cameras. Then the mathematical equation of the uncertainty region is developed based on the correspondence field as a criterion for the localization error including the depth error as well as the X and Y axes error. This field is completely dependent on the internal and external parameters of the cameras. Given the mathematical equation of the localization error, the camera’s configuration optimization is addressed in a stereo-vision system. Finally, the validity of the proposed method is examined by the simulation and empirical results. These results show that the localization error is significantly decreased in the optimized camera configuration.