An FPGA Implementation for a Kalman Filter with Application to Mobile Robotics

The problem of simultaneous localization and mapping has been studied by the mobile robotics scientific community over the last two decades. Most solutions for this problem are based on probabilistic theory in order to represent the uncertainty in robot perception and action. One of the most efficient probabilistic methods is the extended Kalman filter (EKF). However, the EKF demands a considerable amount of computing power and is usually processed by high-end laptops coupled to the robots. In this work, we present an implementation of the EKF targeting an embedded system based on an FPGA device. In order to improve performance, our approach combines a softcore processor with customized hardware. We present experiments with four different FPGA implementations, being the first purely based on software, the second using custom instruction logic directly connected to the processor's ALU, the third using hardware accelerators connected to the processor's data bus, and finally the fourth combining those two hardware/software solutions. For the experiments conducted, the results obtained with a small addition of hardware resources permitted to increase from 2times to 4times the performance of the global system.