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A low-cost stereo system for 3D object recognition

In this paper, we present a low-cost stereo vision system designed for object recognition with FPFH point feature descriptors. Image acquisition is performed using a pair of consumer market UVC cameras costing less than 80 Euros, lacking synchronization signal and without customizable optics. Nonetheless, the acquired point clouds are sufficiently accurate to perform object recognition using FPFH features. The recognition algorithm compares the point cluster extracted from the current image pair with the models contained in a dataset. Experiments show that the recognition rate is above 80% even when the object is partially occluded.