The motivation behind this thesis is to further investigate different approaches for image-based localization. Similarly to PoseNet, we formulate the problem as pose regression and further improve upon it by introducing quaternion algebra for proper attitude representation. In addition, we combine two recently developed approaches: (1) a multi-task loss function that learns the optimal weighting between position and orientation regression tasks, (2) a CNN followed by a spatial LSTM network for better structured feature correlation. Furthermore, we only finetune a small portion of the pretrained CNN feature extractor. Lastly, we extend the problem to videos and employ seq-to-seq regression model based on LSTMs. We evaluate the models on the 7Scenes dataset and introduce a new Airframe dataset, where localization is performed with respect to an object that changes position and orientation in the environment. We achieve at least competitive, but sometimes outperforming results, while requiring considerably less computational power for training the models.

Using multiple UAVs as a collaborative swarm is considerably more effective for target tracking, reconnaissance, and surveillance missions because of their capacity to tackle complex problems synergistically. In this paper, we consider the problem of optimizing three objectives for a swarm of UAVs: (1) target detection and tracking, (2) map coverage, and (3) network connectivity. Our approach, Dual-Pheromone Clustering Hybrid Approach (DPCHA), incorporates a multi-hop clustering and a dual-pheromone ant-colony model to optimize these three objectives. Clustering keeps stable overlay networks, while attractive and repulsive pheromones mark areas of detected targets and visited areas. Extensive simulations show that DPCHA produces significant improvements in the assessment of coverage fairness, cluster stability, and connection volatility. Results show an approximately 50% improvement in map coverage compared to the pure dual-pheromone approach.

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $ 213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing.

Message delivery protocols are fundamental in wireless ad hoc networks. Temporary failures of links and network topology changes should be taken into account, while joining to network should be as simple as possible and maintaining topology should happen in automatic way. In this thesis we propose new communication protocols that provide self-organizing networks and implement them on the XbeePro 868 platform.