Dynamic Bayesian network for semantic place classification in mobile robotics

Abstract

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called Dynamic Bayesian Mixture Model (DBMM), which is an improved variation of the Dynamic Bayesian Network (DBN). More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.

Dynamic Bayesian Network for Time-Dependent Classification Problems in Robotics

“…To give an idea of the dimensionality of X, in semantic place classification [6], the number of features can be nx = 50, while in activity recognition we have 51 features [8]. Given such dimensionalities, which can be even higher, it becomes infeasible to estimate the probability distribution that characterizes P(X | C ) without the use of advanced algorithms.…”

“…In order to demonstrate the use of the DBN as formulated above, we will consider two classification problems that find applications in mobile robotics: semantic place recognition [6] and activity classification [8]. Figure 5 illustrates a probabilistic system for semantic place recognition where data comes from a laser scanner sensor.…”

“…Figure 5 illustrates a probabilistic system for semantic place recognition where data comes from a laser scanner sensor. In a practical application, the sensor is mounted on-board a mobile robot [6,7]. Based on Figure 5, we can make a direct correspondence with the DBN discussed above by verifying that the feature vector is X, the probabilistic classifier outputs the class-conditional probability P(X | C ) and the priors transmit the time-based information through the network.…”

“…This chapter concentrates on inference problems using DBN where the variable to be inferred from a feature vector (data) represents a set of semantic classes = { c 1 , c 2 , … , c nc } or categories, in the context of intelligent perception systems for mobile robotics applications. Namely, we will address problems where denotes semantic places in a given indoor environment [6,7] e.g. = { 'corridor', 'office', … , 'kitchen' } and also when the classes of interest are daily-live activities = { 'drinking', 'talking', … , 'walking' } [8,9].…”

“…feature vectors with hundreds of elements) and also when the distribution that the observed data were drawn is not known (in unseen/ knew or testing scenarios) then, a DBN can be used to handle such complex problems. In robotics, semantic place classification [6,7] and activity recognition [8,9] are examples of such problems and belong to the research area of pattern recognition. For …”