Depth mapping using a low-cost camera array

Abstract

Computer vision has the potential to discern a large amount of information about the environment. This intelligence can be used to make decisions on navigation and obstacle avoidance. One of the core problems in machine vision is determining the distance from the camera to different objects for a given scene. Stereo-vision is one technique for solving this problem. Typically, two cameras are used for this algorithm. Using more than two cameras, however, has the ability to provide even better results. Here, a low-cost array of cameras was used which was built from commonly available, inexpensive hardware. The information from the multiple cameras was combined to provide a dense real-time depth map of the environment. The results of single stereo camera pairs versus multiple stereo camera pairs were compared and it was found that using multiple pairs does provide a denser depth map over that of a single pair.