GPU-Accelerated Path-Dependent Marginal Information Gain for Autonomous Exploration

Autonomous exploration demands that robots continuously evaluate candidate viewpoints based on their expected information gain and execution cost. Sampling-based planners estimate this gain by volumetric raycasting and, due to its computational cost, evaluate candidates under an assumption of mutual independence, ignoring the overlap between viewpoints along the same path.

From the abstract. Our summary is in progress.