FastSLAM Algorithm for Uninhabited Flying Vehicle

Abstract:

For uninhabited flying vehicle, it is a key prerequisite of truly autonomous mobile vehicles to simultaneously localize and accurately map its surroundings. Kalman filter-based algorithms require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents an algorithm so called FastSLAM that recursively estimates the full posterior distribution over robot pose and landmark locations, but scales logarithmically with the number of landmarks in the map. FastSLAM factors the posterior into a product of conditional landmark distributions and a distribution over UAV paths. The algorithm has been tested in UAV environments. Experimental results demonstrate the advantages and disadvantages of the FastSLAM algorithm for UAV.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 3596-3599
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.3596
• Online since: August 2013
• Authors: Lin Ping Feng, Yu Qiang Wu, Shuang Pan, Xi Bin Wang
• Keywords: Extended Kalman Flter (EKF), Fast Simultaneous Location and Mapping (FastSLAM), Particle Filter (PF), Uninhabited Flying Vehicle

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