Feature detection in an indoor environment using Hardware Accelerators for time-efficient Monocular SLAM

Vyas, Shivang

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Feature detection in an indoor environment using Hardware Accelerators for time-efficient Monocular SLAM

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In the field of Robotics, Monocular Simultaneous Localization and Mapping (Monocular SLAM) has gained immense popularity, as it replaces large and costly sensors such as laser range finders with a single cheap camera. Additionally, the well-developed area of Computer Vision provides robust image processing algorithms which aid in developing feature detection technique for the implementation of Monocular SLAM. Similarly, in the field of digital electronics and embedded systems, hardware acceleration using FPGAs, has become quite popular. Hardware acceleration is based upon the idea of offloading certain iterative algorithms from the processor and implementing them on a dedicated piece of hardware such as an ASIC or FPGA, to speed up performance in terms of timing and to possibly reduce the net power consumption of the system. Good strides have been taken in developing massively pipelined and resource efficient hardware implementations of several image processing algorithms on FPGAs, which achieve fairly decent speed-up of the processing time. In this thesis, we have developed a very simple algorithm for feature detection in an indoor environment by means of a single camera, based on Canny Edge Detection and Hough Transform algorithms using OpenCV library, and proposed its integration with existing feature initialization technique for a complete Monocular SLAM implementation. Following this, we have developed hardware accelerators for Canny Edge Detection & Hough Transform and we have compared the timing performance of implementation in hardware (using FPGAs) with an implementation in software (using C++ and OpenCV).

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