We present the systematic design of two linear array IP cores for the k-nearest neighbor (k-NN) benchmark classifier. The need for real-time classification of data vectors with possibly thousands of features (dimensions) motivates the implementation of this widely used algorithm in hardware in order to achieve very high performance by exploiting block pipelining and parallel processing. The two linear array architectures that we designed have been described as soft IP cores in fully parameterizable VHDL that can be used to synthesize effortlessly different k-NN parallel architectures for any desirable combination of the problem size parameters. They have been evaluated for a large variety of parameter combinations and Xilinx FPGAs. It is shown that they can be used to solve efficiently very large size k-NN classification problems, even with thousands of training vectors or vector dimensions, using a single, moderate size FPGA device. Furthermore the FPGA implementations exceed by a factor of two the performance of optimized NVIDIA CUDA API software implementations for the powerful GeForce 8800GTX GPU.
Mars exploration is expected to remain a focus of the scientific community in the years to come. A Mars rover should be highly autonomous because communication between the rover and the terrestrial operation center is difficult, and because the vehicle should spend as much of its traverse time as possible moving. Autonomous behavior of the rover implies that the vision system provides both a wide view to enable navigation and three‐dimensional (3D) reconstruction, and at the same time a close‐up view ensuring safety and providing reliable odometry data. The European Space Agency funded project “SPAring Robotics Technologies for Autonomous Navigation” (SPARTAN) aimed to develop an efficient vision system to cover all such aspects of autonomous exploratory rovers. This paper presents the development of such a system, starting from the requirements up to the testing of the working prototype. The vision system was designed with the intention of being efficient, low‐cost, and accurate and to be implemented using custom‐designed vectorial processing by means of field programmable gate arrays (FPGAs). A prototype of the complete vision system was developed, mounted on a basic mobile robot platform, and tested. The results on both real‐world Mars‐like and long‐range simulated data are presented in terms of 3D reconstruction and visual odometry accuracy, as well as execution speed. The developed system is found to fulfill the set requirements.
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