Information Filter Occupancy Mapping using Decomposable Radial Kernels Building occupancy maps of the environment is a fundamental problem for robot autonomy. A common assumption in early work was that the occupancy states of different map elements are independent. Recently, Gaussian Process (GP) techniques were proposed to capture correlation, which is important not only for improved accuracy but also for uncertainty quantification and autonomous exploration based on the predicted occupancy of nearby unexplored areas. Despite these desirable properties, current GP mapping techniques are limited to small maps and slow inference speeds. This paper proposes an information space formulation of the GP mapping problem. If a decomposable radial kernel is evaluated over a latent grid of pseudoinput points, the resulting kernel matrix has a KroneckerproductofToeplitzmatrices structure that allows very efficient representation of the occupancy distribution. We utilize this structure to design an information filter occupancy mapping algorithm with linear time and memory complexity that still permits continuous space observations and predictions.

Pedestrian Density Prediction for Efficient Mobile Robot Exploration We present a method to predict humans in unexplored map areas given limited observations of the environment. We used a geometric representation of the environment based on cost maps and semantic room categorization. Human density distributions were generated using a human tracker based on LiDAR data recorded by a mobile robot. A Gaussian Process (GP) regression model was created to predict human density in surrounding unobserved map locations. GP prediction performance was evaluated on density data recorded in a series of ten simulations of 25 persons in an office setting, and in realworld robot deployments in an officelike environment. Experimental results demonstrate that the current method can predict human locations with an accuracy average of 70%.