The aim of this paper is to demonstrate the applicability and the effectiveness of a computationally demanding stereo-matching algorithm in different low-cost and low-complexity embedded devices, by focusing on the analysis of timing and image quality performances. Various optimizations have been implemented to allow its deployment on specific hardware architectures while decreasing memory and processing time requirements: (1) reduction of color channel information and resolution for input images; (2) low-level software optimizations such as parallel computation, replacement of function calls or loop unrolling; (3) reduction of redundant data structures and internal data representation. The feasibility of a stereo vision system on a low-cost platform is evaluated by using standard datasets and images taken from infra-red cameras. Analysis of the resulting disparity map accuracy with respect to a full-size dataset is performed as well as the testing of suboptimal solutions.