A variety of platforms, such as micro-unmanned vehicles, are limited in the amount of computational hardware they can support\ndue to weight and power constraints. An efficient stereo vision algorithm implemented on an FPGA would be able to minimize\npayload and power consumption in microunmanned vehicles, while providing 3D information and still leaving computational\nresources available for other processing tasks. This work presents a hardware design of the efficient profile shape matching stereo\nvision algorithm. Hardware resource usage is presented for the targeted micro-UV platform, Helio-copter, that uses the Xilinx\nVirtex 4 FX60 FPGA. Less than a fifth of the resources on this FGPA were used to produce dense disparity maps for image sizes up\nto 450 Ã?â?? 375, with the ability to scale up easily by increasing BRAM usage. A comparison is given of accuracy, speed performance,\nand resource usage of a census transform-based stereo vision FPGA implementation by Jin et al. Results show that the profile shape\nmatching algorithm is an efficient real-time stereo vision algorithm for hardware implementation for resource limited systems such\nas microunmanned vehicles.
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