Curiosity based exploration for learning terrain models. Girdhar, Y., Whitney, D., & Dudek, G. In 2014 IEEE International Conference on Robotics and Automation (ICRA), pages 578-584, 5, 2014. IEEE.
Curiosity based exploration for learning terrain models [pdf]Paper  Curiosity based exploration for learning terrain models [link]Website  abstract   bibtex   
We present a robotic exploration technique in which the goal is to learn to a visual model and be able to distinguish between different terrains and other visual components in an unknown environment. We use ROST, a realtime online spatiotemporal topic modeling framework to model these terrains using the observations made by the robot, and then use an information theoretic path planning technique to define the exploration path. We conduct experiments with aerial view and underwater datasets with millions of observations and varying path lengths, and find that paths that are biased towards locations with high topic perplexity produce better terrain models with high discriminative power, especially with paths of length close to the diameter of the world.

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