Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms

TL;DR

This paper introduces Synthetic Lunar Terrain (SLT), a comprehensive multimodal dataset combining event-based and RGB camera data, along with 3D scans, to advance neuromorphic vision algorithms for lunar exploration tasks.

Contribution

It provides a novel open dataset with synchronized multimodal data, including neuromorphic sensor streams, for training and evaluating lunar-specific vision algorithms.

Findings

SLT enables analysis of neuromorphic versus RGB camera performance.

The dataset supports development of energy-efficient lunar navigation systems.

Neuromorphic data shows resilience in high dynamic range scenes.

Abstract

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.