|Description:||Unmanned Aerial Vehicles (UAVs) are expanding from open outdoor environments into more constrained indoor locations thanks to improvements in accuracy and precision of localization, navigation and control algorithms in recent years. New possibilities for deployment of autonomous UAV swarms into indoor environments emerge, leading to the development of high-level mission-oriented algorithms. Our team is in particular interested in mapping and documenting of historic buildings to assess the condition of the ceiling, murals, statues, stained glass, etc. A reliable position estimate of the UAV is needed for all indoor autonomous flights. Since the global navigation satellite system (GNSS) services are generally not available indoor, the UAV must be localized using onboard sensors only. One of the sensors that are widely used for localization of UAVs, is the rotating laser rangefinder (LIDAR). Two principal approaches exist for laser-scan-based localization: scan matching techniques and feature-based methods.
The goal of this project will be to develop a feature-based simultaneous localization and mapping (SLAM) system. The system will read laser scans from the LIDAR, process them, extract stable features, find corresponding features in the simultaneously built map to estimate the current position of the UAV in the map. A loop closure detection algorithm will be employed to correct the position drift after returning to a previously visited part of the map.