Preliminary Results on 2-D Simultaneous Localization and Mapping for Aerial Robots in Dynamics Environments

Abstract

This paper presents the design and validation of an Extend Kalman Filter (EKF) for Simultaneous Localization and Mapping with Moving Objects Tracking (SLAMMOT) with application to unmanned aerial vehicles (UAVs) in uncertain and dynamic environments. The proposed solution includes the tracking of Moving Objects (MO) using the Multiple Hypothesis Tracking (MHT) method, as well as the identification of the motion models of the environment's objects applying the Interacting Multiple Model (IMM) algorithm. The consistency and performance of the devised SLAMMOT filter is successfully confirmed with simulation results.