Information-theoretic occupancy grid compression for high-speed information-based exploration

We propose information-theoretic strategies for Occupancy Grid (OG) compression to enable high-speed exploration on computationally constrained mobile robots. We first formulate optimal lossy compression for OGs based on the Principle of Relevant Information. The solution to this formulation is a simple compression algorithm that is motivated by rate distortion theory. We then compress OGs to different resolutions and develop a second optimization based on the Information Bottleneck method that chooses a resolution simultaneously maximizing compression and minimizing loss of information from the robot's sensor measurements. On computationally constrained systems, the resulting reduction in computational complexity enables planning over longer predictive horizons, leading to higher-speed operation. Using these techniques to adaptively optimize OG resolution as the robot enters a new area causes it to autonomously slow down in obstacle-dense locations and speed up in open expanses. We simulate and experimentally evaluate mutual information-based exploration through cluttered indoor environments with exploration rates that adapt based on environment complexity, leading to an order-of-magnitude increase in the maximum rate of exploration in contrast to non-adaptive techniques given the same finite computational resources.