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It has been more than two decades since the first neuromorphic Dynamic Vision Sensor (DVS) sensor was invented, and many subsequent prototypes have been built with a wide spectrum of applications in mind. Competing against state-of-the-art neural networks in terms of accuracy is difficult, although there are clear opportunities to outperform conventional approaches in terms of power consumption and processing speed. As neuromorphic sensors generate sparse data at the focal plane itself, they are inherently energy-efficient, data-driven, and fast. In this work, we present an extended DVS pixel simulator for neuromorphic benchmarks which simplifies the latency and the noise models. In addition, to more closely model the behaviour of a real pixel, the readout circuitry is modelled, as this can strongly affect the time precision of events in complex scenes. Using a dynamic variant of the MNIST dataset as a benchmarking task, we use this simulator to explore how the latency of the sensor allows it to outperform conventional sensors in terms of sensing speed.

Earth orbit is a limited natural resource that hosts a vast range of vital space-based systems that support the international community's national, commercial and defence interests. This resource is rapidly becoming depleted with over-crowding in high demand orbital slots and a growing presence of space debris. We propose the Fast Iterative Extraction of Salient targets for Tracking Asynchronously (FIESTA) algorithm as a robust, real-time and reactive approach to optical Space Situational Awareness (SSA) using Event-Based Cameras (EBCs) to detect, localize, and track Resident Space Objects (RSOs) accurately and timely. We address the challenges of the asynchronous nature and high temporal resolution output of the EBC accurately, unsupervised and with few tune-able parameters using concepts established in the neuromorphic and conventional tracking literature. We show this algorithm is capable of highly accurate in-frame RSO velocity estimation and average sub-pixel localization in a simulated test environment to distinguish the capabilities of the EBC and optical setup from the proposed tracking system. This work is a fundamental step toward accurate end-to-end real-time optical event-based SSA, and developing the foundation for robust closed-form tracking evaluated using standardized tracking metrics.

An event-based image sensor works dramatically differently from the conventional frame-based image sensors in a way that it only responds to local brightness changes whereas its counterparts’ output is a linear representation of the illumination over a fixed exposure time. The output of an event-based image sensor therefore is an asynchronous stream of spatial-temporal events data tagged with the location, timestamp and polarity of the triggered events. Compared to traditional frame-based image sensors, event-based image sensors have advantages of high temporal resolution, low latency, high dynamic range and low power consumption. Although event-based image sensors have been used in many computer vision, navigation and even space situation awareness applications, little work has been done to explore their applicability in the field of wavefront sensing. In this work, we present the integration of an event camera in a Shack-Hartmann wavefront sensor and the usage of event data to determine spot displacement and wavefront estimation. We show that it can achieve the same functionality but with substantial speed and can operate in extremely low light conditions. This makes an event-based Shack-Hartmann wavefront sensor a preferable choice for adaptive optics systems where light budget is limited or high bandwidth is required.

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