On the Generation of a Synthetic Event-Based Vision Dataset for
  Navigation and Landing
Loïc J. Azzalini Emmanuel Blazquez Alexander Hadjiivanov Gabriele Meoni Dario Izzo
An event-based camera outputs an event whenever a change in scene brightness
of a preset magnitude is detected at a particular pixel location in the sensor
plane. The resulting sparse and asynchronous output coupled with the high
dynamic range and temporal resolution of this novel camera motivate the study
of event-based cameras for navigation and landing applications. However, the
lack of real-world and synthetic datasets to support this line of research has
limited its consideration for onboard use. This paper presents a methodology
and a software pipeline for generating event-based vision datasets from optimal
landing trajectories during the approach of a target body. We construct
sequences of photorealistic images of the lunar surface with the Planet and
Asteroid Natural Scene Generation Utility at different viewpoints along a set
of optimal descent trajectories obtained by varying the boundary conditions.
The generated image sequences are then converted into event streams by means of
an event-based camera emulator. We demonstrate that the pipeline can generate
realistic event-based representations of surface features by constructing a
dataset of 500 trajectories, complete with event streams and motion field
ground truth data. We anticipate that novel event-based vision datasets can be
generated using this pipeline to support various spacecraft pose reconstruction
problems given events as input, and we hope that the proposed methodology would
attract the attention of researchers working at the intersection of
neuromorphic vision and guidance navigation and control.