A Floating-point Extended Kalman Filter Implementation for Autonomous Mobile Robots

Authors:
Vanderlei Bonato;Eduardo Marques;George A. Constantinides
Affiliations:
Institute of Mathematical and Computing Sciences, The University of São Paulo, São Carlos, Brazil;Institute of Mathematical and Computing Sciences, The University of São Paulo, São Carlos, Brazil;Department of Electrical and Electronic Engineering, Imperial College London, London, UK
Venue:
Journal of Signal Processing Systems
Year:
2009

Citing 6
Cited 3

Estimating uncertain spatial relationships in robotics

Autonomous robot vehicles
Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision
Embedded Robotics: Mobile Robot Design and Applications with Embedded Systems

Embedded Robotics: Mobile Robot Design and Applications with Embedded Systems
Towards an Embedded Visuo-Inertial Smart Sensor

International Journal of Robotics Research
Bayesian Filtering for Location Estimation

IEEE Pervasive Computing
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments

IEEE Transactions on Robotics

An FPGA implementation of the SMG-SLAM algorithm

Microprocessors & Microsystems
FPGA implementation of Kalman filter for neural ensemble decoding of rat's motor cortex

Neurocomputing
FPGA based efficient on-chip memory for image processing algorithms

Microelectronics Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 algorithm that is capable of processing two-dimensional maps containing up to 1.8 k features at real time (14 Hz), a three-fold improvement over a Pentium M 1.6 GHz, and a 13-fold improvement over an ARM920T 200 MHz. The proposed architecture also consumes only 1.3% of the Pentium and 12.3% of the ARM energy per feature.