A review of SLAM techniques and applications in unmanned aerial vehicles

Abstract

Abstract Simultaneous Localisation and Mapping (SLAM) is a foundational idea in the field of robotics. It involves the processing of sensor signals and the optimisation of pose-graphs. SLAM has found several applications in various domains, including but not limited to courier services, agriculture, environmental monitoring, and military operations, particularly with the use of Unmanned Aerial Vehicles (UAVs). There exist several applications. This work aims to provide a comprehensive analysis of three Simultaneous Localization and Mapping (SLAM) algorithms, namely CNN-SLAM, Linearized Kalman Filter (LKF), and Extended Kalman Filter (EKF). Additionally, it will explore the utilisation of SLAM algorithms in Unmanned Aerial Vehicles (UAVs) by examining its use in precision agriculture, geological surveys, and Emergency Scenarios. This section will outline certain issues that SLAM algorithms may encounter in relation to wide area applications, real-time processing and efficiency, robustness, and dynamic objects within the environment. Ultimately, this study will undertake a comparative analysis of the merits and drawbacks associated with the three algorithms, while also putting up potential remedies for the aforementioned issues.