Abstract
In recent years the interest on monocular-SLAM (Simultaneous Localization and Mapping) has increased, this because nowadays it is possible to find inexpensive, small and light commercial cameras and they provide visual environmental information that can be exploited to create 3D maps and camera pose in an unknown environment. A smart camera that could deliver monocular-SLAM is highly desirable, since it can be the basis of several robotics/drone applications. In this article, we present a new SLAM framework that is robust enough for indoor/outdoor SLAM applications, and at the same time is parallelizable in the context of FPGA architecture design. We introduce new feature-extraction/feature-matching algorithms, suitable for FPGA implementation. We propose an FPGA based sensor-processor architecture where most of the visual processing is carried out in a parallel architecture, and the 3D map construction and camera pose estimation in the processor of a SoC FPGA. An FPGA architecture is lay down and hardware/software partition is discussed. We show that the proposed sensor-processor can deliver high performance under several indoor/outdoor scenarios.
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