Title :
Performance analysis of filter based airborne simultaneous localization and mapping methods
Author :
Duymaz, Erol ; Oguz, A. Ersan ; Temeltas, Hakan
Author_Institution :
Electron. Eng. Dept., Turkish Air Force Acad., Istanbul, Turkey
Abstract :
In this research simultaneous localization and mapping (SLAM) problem of unmanned systems which has emerged in last decade is identified by detecting SLAM algorithms particularly in air vehicle platforms and particle filter based SLAM implementation of aerial systems is first introduced as well. Regarding to survey consequences the variety of SLAM applications span from parametric filters such as Unscented Kalman Filter, Extended Kalman Filter to nonparametric such as Particle Filter and concerning diversity of vision based approaches that aims up level control and variety of sensors that unmanned vehicles carry a taxonomy is a requirement for better comprehension of SLAM performances. Although it is not aimed to compare performance of all SLAM methods for problem of Airborne-SLAM (A-SLAM) navigation in GNSS denied environment the scan of indexed papers suggests via providing brief background such as Kalman and particle filter based Simultaneous Localization and Mapping (SLAM) approach formulations or simulations that best SLAM algorithm can only be identified in reference to the scenario which differs in environment, platform, vehicle, sensor...etc. while key findings of Unscented Kalman Filter (UKF), Extended Kalman Filter (EKF) and Particle Filter (PF) Based A-SLAM structures give that Particle Filter (PF) Based A-SLAM may be superior to others in some scenarios principally depending on particle number.
Keywords :
Kalman filters; SLAM (robots); particle filtering (numerical methods); remotely operated vehicles; satellite navigation; GNSS; air vehicle platforms; airborne-SLAM navigation; extended Kalman filter; filter based airborne simultaneous localization and mapping methods; particle filter; particle filter based SLAM implementation; unmanned vehicle systems; unscented Kalman filter; Conferences; Global Positioning System; Kalman filters; Particle filters; Simultaneous localization and mapping; Vehicles; Airborne SLAM; Extended Kalman Filter; GNSS denied Environment; Particle Filter Based SLAM; UAV Autonomous Navigation; Uncscented Kalman Filter;
Conference_Titel :
Recent Advances in Space Technologies (RAST), 2015 7th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4673-7760-7
DOI :
10.1109/RAST.2015.7208333