Indoor place classification for intelligent mobile systems

University of Technology Sydney. Faculty of Engineering and Information Technology.Place classification is an emerging theme in the study of human-robot interaction which requires common understanding of human-defined concepts between the humans and machines. The requirement posts a significant challenge to the current intelligent mobile systems which are more likely to be operating in absolute coordinate systems, and hence unaware of the semantic labels. Aimed at filling this gap, the objective of the research is to develop an approach for intelligent mobile systems to understand and label the indoor environments in a holistic way based on the sensory observations. Focusing on commonly available sensors and machine learning based solutions which play a significant role in the research of place classification, solutions to train a machine to assign unknown instances with concepts understandable to human beings, like room, office and corridor, in both independent and structured prediction ways, have been proposed in this research. The solution modelling dependencies between random variables, which takes the spatial relationship between observations into consideration, is further extended by integrating the logical coexistence of the objects and the places to provide the machine with the additional object detection ability. The main techniques involve logistic regression, support vector machine, and conditional random field, in both supervised and semi-supervised learning frameworks. Experiments in a variety of environments show convincing place classification results through machine learning based approaches on data collected with either single or multiple sensory modalities; modelling spatial dependencies and introducing semi-supervised learning paradigm further improve the accuracy of the prediction and the generalisation ability of the system; and vision-based object detection can be seamlessly integrated into the learning framework to enhance the discrimination ability and the flexibility of the system. The contributions of this research lie in the in-depth studies on the place classification solutions with independent predictions, the improvements on the generalisation ability of the system through semi-supervised learning paradigm, the formulation of training a conditional random field with partially labelled data, and the integration of multiple cues in two sensory modalities to improve the system's functionality. It is anticipated that the findings of this research will significantly enhance the current capabilities of the human robot interaction and robot-environment interaction

Dynamic Bayesian network for semantic place classification in mobile robotics

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. 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

Learning part-based spatial models for laser-vision-based room categorization

Room categorization, that is, recognizing the functionality of a never before seen room, is a crucial capability for a household mobile robot. We present a new approach for room categorization that is based on two-dimensional laser range data. The method is based on a novel spatial model consisting of mid-level parts that are built on top of a low-level part-based representation. The approach is then fused with a vision-based method for room categorization, which is also based on a spatial model consisting of mid-level visual parts. In addition, we propose a new discriminative dictionary learning technique that is applied for part-dictionary selection in both laser-based and vision-based modalities. Finally, we present a comparative analysis between laser-based, vision-based, and laser-vision-fusion-based approaches in a uniform part-based framework, which is evaluated on a large dataset with several categories of rooms from domestic environments. </jats:p

Application of semi-supervised learning with Voronoi Graph for place classification

Representation of spaces including both geometric and semantic information enables a robot to perform high-level tasks in complex environments. Therefore, in recent years identifying and semantically labeling the environments based on onboard sensors has become an important competency for mobile robots. Supervised learning algorithms have been extensively used for this purpose with SVM-based solutions showing good generalization properties. The CRF-based approaches take the advantage of connectivity information of samples thereby provide a mechanism to capture complex dependencies. Blending the complementary strengths of Support Vector Machine (SVM) and Conditional Random Field (CRF), there have been algorithms to exploit the advantages of both to enhance the overall accuracy of place classification in indoor environments. However, experiments show that none of the above approaches deal well with diversified testing data. In this paper, we focus mainly on the generalization ability of the model and propose a semi-supervised learning strategy, which essentially improves the performance of the system. Experiments have been carried out on six real-world maps from different universities around the world and the results from rigorous testing demonstrate the feasibility of the approach. © 2012 IEEE