Neuromorphic Vision-Based Dataset for Soil Characterization and Slip Estimation in Rover Applications

Most rovers rely on frame-based cameras which exhibit considerable limitations like high power consumption, extensive storage demands and degraded visual clarity under low light conditions.

To address these limitations, we introduce a novel rover perception approach using an neuromorphic camera which offers microsecond latency, high temporal resolution, and low power consumption. A neuromorphic vision-based dataset is presented that will significantly automate two fundamental navigation tasks such as soil characterization and slip estimation. The dataset comprises 270 experiments collected over five different speeds, two lighting conditions, three soil types and two failure scenarios, capturing wheel-terrain interactions.