Learning Continuous Occupancy Maps with the Ising Process Model

TL;DR

This paper introduces a computationally efficient method for learning continuous occupancy maps for robot navigation by generalizing the Ising model, effectively modeling spatial correlations with minimal hyperparameters.

Contribution

It presents a novel application of the Ising process model for continuous occupancy mapping, reducing computational costs and hyperparameter complexity compared to existing methods.

Findings

Effective in simulated indoor environments with known ground truth

Demonstrated on large indoor and outdoor datasets

Requires only a few hyperparameters for training

Abstract

We present a new method of learning a continuous occupancy field for use in robot navigation. Occupancy grid maps, or variants of, are possibly the most widely used and accepted method of building a map of a robot's environment. Various methods have been developed to learn continuous occupancy maps and have successfully resolved many of the shortcomings of grid mapping, namely, priori discretisation and spatial correlation. However, most methods for producing a continuous occupancy field remain computationally expensive or heuristic in nature. Our method explores a generalisation of the so-called Ising model as a suitable candidate for modelling an occupancy field. We also present a unique kernel for use within our method that models range measurements. The method is quite attractive as it requires only a small number of hyperparameters to be trained, and is computationally efficient. The small number of hyperparameters can be quickly learned by maximising a pseudo likelihood. The technique is demonstrated on both a small simulated indoor environment with known ground truth as well as large indoor and outdoor areas, using two common real data sets.