The estimation of optical flow, which determines the movement of objects in a visual scene, is a crucial problem in computer vision. It is essential for applications such as autonomous navigation, where precise motion estimation is critical for performance and safety.

Frame-based cameras capture sequences of still images at regular intervals, from which optical flow is traditionally extracted using optimization-based or learning-based methods. Recently, event-based cameras, which detect changes in pixel brightness asynchronously, have gained traction due to their high temporal resolution and robustness to motion blur, and many algorithms have been developed to estimate optical flow from this data. IDNet is a learning-based approach that achieves state-of-the-art performance. However, IDNet and similar models face two major challenges: they require labeled ground-truth data for training, which is scarce and difficult to collect, and they rely on recurrent neural networks (RNNs) with a fixed number of refinement iterations. This fixed iteration scheme does not adapt to scene complexity, limiting accuracy for complex flows and increasing computational effort for simpler patterns.

The aim of this project is to explore, implement, and evaluate potential methods to address these two mentioned limitations and enhance the capabilities of models like IDNet.

To remove the need for ground-truth data, a self-supervised learning paradigm was implemented by introducing a novel contrast maximization loss that assesses the blur present when accumulating raw events for a certain time interval and compensating it with the predicted flow. To assess the effectiveness of this method, models were trained on the benchmark MVSEC dataset, showing improved results over previous methods with up to 15% on some sequences and an 8% improvement on average. Based on these experiments and results, further research directions were proposed.

As for the problem of the current fixed iteration scheme, Deep Equilibrium Models were found to provide a promising pathway to solving it. These novel models reformulate their iterative structure into a root-finding problem and utilize traditional solvers to find a solution based on some tolerance, providing a trade-off between speed and accuracy. Moreover, they allow for direct differentiation through the network using only their final estimate, compared to previous methods that keep track of their state through all iterations, leading to an O(1) memory consumption. Implementing these and some additional ideas, the trained DEQ IDNet model reached competitive performance on the DSEC dataset while consuming 15% less memory. Yet, further work is needed to close the gap and achieve state-of-the-art performance.

Although deep reinforcement learning (DRL) is a highly promising approach to learning robotic vision-based control, it is plagued by long training times. This report introduces a DRL setup that relies on self-supervised learning for extracting depth information valuable for navigation. Specifically, a literature study is conducted to investigate the effects of learning how to synthesize one view from the other in a stereo-vision setup without relying on any preliminary knowledge of the camera extrinisics and how it can be integrated for its downstream use for an obstacle avoidance task. As such, the literature study concludes that competitive geometry-free monocular-to-stereo image view synthesis is feasible due to recent developments in computer vision. The scientific paper further develops concepts proposed in the literature study and benchmarks the proposed architectures on depth estimation benchmarks for KITTI. Competitive results are achieved for view synthesis and despite sub-optimal performance compared to state-of-the-art monocular depth estimation, an ability to encode depth and detect shapes is present and, therefore, satisfactory for the application to DRL. Additionally, the research examines the benefits of using the latent space of a view synthesis architecture compared to other feature extractor methods as an input to the PPO agent implemented as auxiliary tasks. This method achieves quicker convergence and better performance for an obstacle avoidance task in a simulated indoor environment than the autoencoding feature extractor and end-to-end DRL methods. It is only outperformed by the monocular depth estimation feature extractor method. Overall, this research provides valuable insights for developing more efficient and effective DRL methods for monocular camera-based drones. Finally, the complementary code for this research can be found: \url{https://github.com/ldenridder/drl-obstacle-avoidance-view-synthesis}.

Maintaining a high speed, secure and robust connection to the world is of paramount importance
in modern society. This proved especially true during a recent volcanic eruption in the small island nation of Tonga, where the only optical fibre line to the country was severed due to the cataclysm, completely disconnecting the island from the rest of the world. The lack of communication made the disaster relief to the eruption more complex. The Emergency Communications Node (ECN) can be deployed within 48 hours to enable alternative means of communication that replace damaged communication lines...