A Floating-point Extended Kalman Filter Implementation for Autonomous Mobile Robots
Contribuinte(s) |
UNIVERSIDADE DE SÃO PAULO |
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Data(s) |
20/10/2012
20/10/2012
2009
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Resumo |
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. CAPES[BEX2683/06-7] Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) EPSRC[EP/C549481/1] EPSRC EPSRC EPSRC[EP/C512596/1] |
Identificador |
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY, v.56, n.1, p.41-50, 2009 1939-8018 http://producao.usp.br/handle/BDPI/28981 10.1007/s11265-008-0257-8 |
Idioma(s) |
eng |
Publicador |
SPRINGER |
Relação |
Journal of Signal Processing Systems for Signal Image and Video Technology |
Direitos |
restrictedAccess Copyright SPRINGER |
Palavras-Chave | #Mobile robotics #SLAM #EKF #FPGA #SIMULTANEOUS LOCALIZATION #Computer Science, Information Systems #Engineering, Electrical & Electronic |
Tipo |
article original article publishedVersion |