Feature-based SLAM algorithm for small scale UAV with nadir view

Small-scale Unmanned Aerial Vehicles (UAVs) have recently been used in several application areas, including search and rescue operations, precision agriculture, and environmental monitoring. Telemetry data, acquired by GPSs, plays a key role in supporting activities in areas like those just reported. In particular, this data is often used for the real-time computation of UAVs paths and heights, which are basic pre-requisites for many tasks. In some cases, however, the GPS sensors can lose their satellite connection, thus making the telemetry data acquisition impossible. This paper presents a feature-based Simultaneous Localisation and Mapping (SLAM) algorithm for small-scale UAVs with nadir view. The proposed algorithm allows to know the travelled route as well as the flight height by using both a calibration step and visual features extracted from the acquired images. Due to the novelty of the proposed algorithm no comparisons with other methods are reported. Anyway, extensive experiments on the recently released UAV Mosaicking and Change Detection (UMCD) dataset have shown the effectiveness and robustness of the proposed algorithm. The latter and the dataset can be used as baseline for future research in this application area.