Novelty detection and segmentation based on Gaussian mixture models: a case study in 3D robotic laser mapping

Abstract

This article proposes a framework to detect and segment changes in robotics datasets, using 3D robotic mapping as a case study. The problem is very relevant in several application domains, not necessarily related with mobile robotics, including security, health, industry and military applications. The aim is to identify significant changes by comparing current data with previous data provided by sensors. This feature is extremely challenging because large amounts of noisy data must be processed in a feasible way. The proposed framework deals with novelty detection and segmentation in robotic maps using clusters provided by Gaussian Mixture Models (GMMs). GMMs provides a feature space that enables data compression and effective processing. Two alternative criteria to detect changes in the GMM space are compared: a greedy technique based on the Earth Mover’s Distance (EMD); and a structural matching algorithm that fulfills both absolute (global matching) and relative constraints (structural matching). The proposed framework is evaluated with real robotic datasets and compared with other methods known from literature. With this purpose, 3D mapping experiments are carried out with both simulated data and real data from a mobile robot equipped with a 3D range sensor.