Freiburg Terrains
Introduced by Zürn et al. in Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning
Freiburg Terrains consist of three parts: 3.7 hours of audio recordings of the microphone pointed at the robot wheels. It also contains 24K RGB images from the camera mounted on top of the robot. The dataset creators also provide the SLAM poses for each data collection run. The dataset can be used for terrain classification which is useful for agent navigation tasks.
Source: http://deepterrain.cs.uni-freiburg.de/Papers
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