FPGA implementation of a sequential Extended Kalman Filter algorithm applied to mobile robotics localization problem

This work describes a hardware architecture for implementing a sequential approach of the Extended Kalman Filter (EKF) that is suitable for mobile robotics tasks, such as self-localization, mapping, and navigation problems. As such algorithm is computationally intensive, commonly it is implemented in Personal Computer (PC)-based platform to be employed on larger robots. In order to allow the development of small robotic platforms, as those required in many current state of the art research (for instance microrobotics area), small size, low-power and high floating-point computing capability targets are required, as well as specific architectures designed for them. Thus, the proposed architecture has been achieved, for self-localization task, using floating-point arithmetic operators (in simple precision), allowing the fusion of data coming from different sensors such as ultrasonic (Sonar) and Laser Range Finder (LRF). The system has been adapted for achieving a reconfigurable platform, and applied to a Pioneer 3-AT mobile robot.