Localization and mapping are two of most important capabilities of autonomous mobile robots and an important method for dealing with these problems is the Extended Kalman Filter (EKF - Extended Kalman Filter), which simultaneously maps the environment, estimating a model for this and robot's position. This problem is known in literature as SLAM (simultaneous localization and mapping). In contrast, the EKF, is typically implemented using floating-point representations, possess a higher order computer ((n2)) where n is the number of elements on the map) so that, for large map, requires a computer system with high computational power to handle the solution of the SLAM-time navigation (online). To contour these restrictions, this project aims to implement a hardware architecture and software for the EKF-SLAM with fixed-point arithmetic in hardware / FPGA (Field-Programmable Gate Array), allowing it to be embedded in mobile robots to take advantage better computational resources.
News published in Agência FAPESP Newsletter about the scholarship: