ON FPGA BASED ACCELERATION OF IMAGE PROCESSING IN MOBILE ROBOTICS

In visual navigation tasks, a lack of the computational resources is one of the main limitations of micro robotic platforms to be deployed in autonomous missions. It is because the most of nowadays techniques of visual navigation relies on a detection of salient points that is computationally very demanding. In this paper, an FPGA assisted acceleration of image processing is considered to overcome limitations of computational resources available on-board and to enable high processing speeds while it may lower the power consumption of the system. The paper reports on performance evaluation of the CPU–based and FPGA–based implementations of a visual teach-and-repeat navigation system based on detection and tracking of the FAST image salient points. The results indicate that even a computationally efficient FAST algorithm can benefit from a parallel (low–cost) FPGA–based implementation that has a competitive processing time but more importantly it is a more power efficient

On-board Inertial-assisted Visual Odometer on an Embedded System

In this paper, we propose a novel inertial-assisted visual odometry system intended for low-cost micro aerial vehicles (MAVs). The system sensor assembly consists of two downward-facing cameras and an inertial measurement unit (IMU) with three-axis accelerometers/gyroscopes. Real-time implementation of the system is enabled by a low-cost embedded system via two important features: firstly, simple pixel-level algorithms are integrated in a low-end FPGA and accelerated via pipeline and combinational logic techniques; secondly, a fast yaw-and-translation estimation algorithm works well with a novel outlier rejection scheme based on probabilistic predetermined operations rather than hypothesis testing iterations. We illustrate the performance of our system by hovering a MAV in a GPS-denied environment. Its feasibility and robustness is also illustrated in complex outdoor environments