A floating-point Extended Kalman Filter implementation for autonomous mobile robots

Localization and Mapping are two of the most important capabilities for autonomous mobile robots and have been receiving considerable attention from the scientific computing community over the last 10 years. One of the most efficient methods to address these problems is based on the use of the Extended Kalman Filter (EKF). The EKF simultaneously estimates a model of the environment (map) and the position of the robot based on odometric and exteroceptive sensor information. As this algorithm demands a considerable amount of computation, it is usually executed on high end PCs coupled to the robot. In this work we present an FPGA-based architecture for the EKF

• Type: Conference
• Year: 2007
• Where: FPL
• Authors: Vanderlei Bonato, Eduardo Marques, George A. Constantinides
• Keywords: Autonomous Mobile Robots, Extended Kalman Filter, FPL 2007, Hardware, Scientific Computing Community