Efficient Mobile Robot Exploration with Gaussian Markov Random Fields in 3D Environments

In this paper, we study the problem of autonomous exploration in unknown indoor environments using mobile robot. We use mutual information (MI) to evaluate the information the robot would get at a certain location. In order to get the most informative sensing location, we first propose a sampling method that can get random sensing patches in free space. Each sensing patch is extended to informative locations to collect information with true values. Then we use Gaussian Markov Random Fields (GMRF) to model the distribution of MI in environment. Compared with the traditional methods that employ Gaussian Process (GP) model, GMRF is more efficient. MI of every sensing location can be estimated using the training sample patches and the established GMRF model. We utilize an efficient computation algorithm to estimate the GMRF model hyperparameters so as to speed up the computation. Besides the information gain of the candidates regions, the path cost is also considered in this work. We propose a utility function that can balance the path cost and the information gain the robot would collect. We tested our algorithm in both simulated and real experiment. The experiment results demonstrate that our proposed method can explore the environment efficiently with relatively shorter path length.