SLAM (robots);aerospace control;autonomous aerial vehicles;graph theory;image sensors;mobile robots;optimisation;robot vision;stereo image processing;telerobotics;SLAM;autonomous navigation;cluttered environments;graph nodes;loop closure detection;online mapping;open field environments;optimization algorithm;primary sensor;real-time graph;small UAV;small unmanned aerial vehicles;stereo camera;unknown environments;unknown outdoor environments;Cameras;Real-time systems; ;Robot vision systems;Simultaneous localization and mapping;Three-dimensional displays;Unmanned aerial vehicles
[en] Autonomous navigation of small Unmanned Aerial Vehicles (UAVs) in cluttered environments is still a challenging problem. In this work, we present an approach based on graph slam and loop closure detection for online mapping of unknown outdoor environments using a small UAV. Here, we used an onboard front facing stereo camera as the primary sensor. The data extracted by the cameras are used by the graph-based slam algorithm to estimate the position and create the graph-nodes and construct the map. To avoid multiple detections of one object as different objects and to identify re-visited locations, a loop closure detection is applied with optimization algorithm using the g2o toolbox to minimize the error. Furthermore, 3D occupancy map is used to represent the environment. This technique is used to save memory and computational time for the online processing. Real experiments are conducted in outdoor cluttered and open field environments.The experiment results show that our presented approach works under real time constraints, with an average time to process the nodes of the 3D map is 17.79ms.