Performance Analysis of Filter Based Airborne Simultaneous Localization and Mapping Methods

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.