Information-theoretic mapping using Cauchy-Schwarz Quadratic Mutual Information

We develop a computationally efficient control policy for active perception that incorporates explicit models of sensing and mobility to build 3D maps with ground and aerial robots. Like previous work, our policy maximizes an information-theoretic objective function between the discrete occupancy belief distribution (e.g., voxel grid) and future measurements that can be made by mobile sensors. However, our work is unique in three ways. First, we show that by using Cauchy-Schwarz Quadratic Mutual Information (CSQMI), we get significant gains in efficiency. Second, while most previous methods adopt a myopic, gradient-following approach that yields poor convergence properties, our algorithm searches over a set of paths and is less susceptible to local minima. In doing so, we explicitly incorporate models of sensors, and model the dependence (and independence) of measurements over multiple time steps in a path. Third, because we consider models of sensing and mobility, our method naturally applies to both ground and aerial vehicles. The paper describes the basic models, the problem formulation and the algorithm, and demonstrates applications via simulation and experimentation.