Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms
This provides a domain-specific dataset for researchers in space robotics and neuromorphic computing, but it is incremental as it applies existing methods to new data.
The authors introduced the Synthetic Lunar Terrain (SLT) dataset, which includes event-based and RGB camera captures with 3D laser scans, to address the need for training and evaluating neuromorphic vision algorithms for lunar applications like rover navigation and landing in cratered environments, resulting in a publicly available resource.
Synthetic Lunar Terrain (SLT) is an open dataset collected from an analogue test site for lunar missions, featuring synthetic craters in a high-contrast lighting setup. It includes several side-by-side captures from event-based and conventional RGB cameras, supplemented with a high-resolution 3D laser scan for depth estimation. The event-stream recorded from the neuromorphic vision sensor of the event-based camera is of particular interest as this emerging technology provides several unique advantages, such as high data rates, low energy consumption and resilience towards scenes of high dynamic range. SLT provides a solid foundation to analyse the limits of RGB-cameras and potential advantages or synergies in utilizing neuromorphic visions with the goal of enabling and improving lunar specific applications like rover navigation, landing in cratered environments or similar.