The semantic localization problem in robotics consists in determining the place where a robot is located by means of semantic categories. The problem is usually addressed as a supervised classification process, where input data correspond to robot perceptions while classes to semantic categories, like kitchen or corridor.In this paper we propose a framework, implemented in the PCL library, which provides a set of valuable tools to easily develop and evaluate semantic localization systems. The implementation includes the generation of 3D global descriptors following a Bag-of-Words approach. This allows the generation of fixed-dimensionality descriptors from any type of keypoint detector and feature extractor combinations. The framework has been designed, structured and implemented to be easily extended with different keypoint detectors, feature extractors as well as classification models.The proposed framework has also been used to evaluate the performance of a set of already implemented descriptors, when used as input for a specific semantic localization system. The obtained results are discussed paying special attention to the internal parameters of the BoW descriptor generation process. Moreover, we also review the combination of some keypoint detectors with different 3D descriptor generation techniques. Presentation of a BoW implementation in the Point Cloud Library.Proposal of a general framework for semantic localization systems.The framework allows for integrations of future 3D features and keypoints.The Harris3D detector outperforms uniform sampling with fewer detected keypoints.BoW descriptors obtain better results than the ESF global feature.
As already mentioned, the semantic localization problem consists of the process of acquiring an image, generate a suitable representation (that is, an image descriptor) and classifying the imaged scene @cite_10 . This classification can be performed according to a) high-level features of the environment, like detected objects @cite_21 @cite_5 @cite_19 , b) global image representations @cite_30 , or c) local features @cite_29 . @cite_13 a method for scene classification based on global image features was presented, where the temporal continuity between consecutive images was exploited using a Hidden Markov Model. @cite_8 , a scene classifier with range data as input information and AdaBoost as the classification model is proposed. In 2006, @cite_23 developed a visual scene classifier using composed receptive field histograms @cite_6 and SVMs.
@cite_30 @cite_13 @cite_8 @cite_29 @cite_21 @cite_6 @cite_19 @cite_23 @cite_5 @cite_10
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Humans can recognize the gist of a novel image in a single glance, independent of its complexity. How is this remarkable feat accomplished? On the basis of behavioral and computational evidence, this paper describes a formal approach to the representation and the mechanism of scene gist understanding, based on scene-centered, rather than object-centered primitives. We show that the structure of a scene image can be estimated by the mean of global image features, providing a statistical summary of the spatial layout properties (Spatial Envelope representation) of the scene. Global features are based on configurations of spatial scales and are estimated without invoking segmentation or grouping operations. The scene-centered approach is not an alternative to local image analysis but would serve as a feed-forward and parallel pathway of visual processing, able to quickly constrain local feature analysis and enhance object recognition in cluttered natural scenes. While navigating in an environment, a vision system has to be able to recognize where it is and what the main objects in the scene are. We present a context-based vision system for place and object recognition. The goal is to identify familiar locations (e.g., office 610, conference room 941, main street), to categorize new environments (office, corridor, street) and to use that information to provide contextual priors for object recognition (e.g., tables are more likely in an office than a street). We present a low-dimensional global image representation that provides relevant information for place recognition and categorization, and show how such contextual information introduces strong priors that simplify object recognition. We have trained the system to recognize over 60 locations (indoors and outdoors) and to suggest the presence and locations of more than 20 different object types. The algorithm has been integrated into a mobile system that provides realtime feedback to the user. This paper addresses the problem of classifying places in the environment of a mobile robot into semantic categories. We believe that semantic information about the type of place improves the capabilities of a mobile robot in various domains including localization, path-planning, or human-robot interaction. Our approach uses AdaBoost, a supervised learning algorithm, to train a set of classifiers for place recognition based on laser range data. In this paper we describe how this approach can be applied to distinguish between rooms, corridors, doorways, and hallways. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various environments. In this survey, we give an overview of invariant interest point detectors, how they evolvd over time, how they work, and what their respective strengths and weaknesses are. We begin with defining the properties of the ideal local feature detector. This is followed by an overview of the literature over the past four decades organized in different categories of feature extraction methods. We then provide a more detailed analysis of a selection of methods which had a particularly significant impact on the research field. We conclude with a summary and promising future research directions. Presented at the 2007 Robotics: Science and Systems Conference III (RSS), 27-30 June 2007, Atlanta, GA. Effective methods for recognising objects or spatio-temporal events can be constructed based on receptive field responses summarised into histograms or other histogram-like image descriptors. This work presents a set of composed histogram features of higher dimensionality, which give significantly better recognition performance compared to the histogram descriptors of lower dimensionality that were used in the original papers by Swain & Bollard (1991) or Schiele & Crowley (2000). The use of histograms of higher dimensionality is made possible by a sparse representation for efficient computation and handling of higher-dimensional histograms. Results of extensive experiments are reported, showing how the performance of histogram-based recognition schemes depend upon different combinations of cues, in terms of Gaussian derivatives or differential invariants applied to either intensity information, chromatic information or both. It is shown that there exist composed higher-dimensional histogram descriptors with much better performance for recognising known objects than previously used histogram features. Experiments are also reported of classifying unknown objects into visual categories. Mobile robotics has achieved notable progress, however, to increase the complexity of the tasks that mobile robots can perform in natural environments, we need to provide them with a greater semantic understanding of their surrounding. In particular, identifying indoor scenes, such as an Office or a Kitchen, is a highly valuable perceptual ability for an indoor mobile robot, and in this paper we propose a new technique to achieve this goal. As a distinguishing feature, we use common objects, such as Doors or furniture, as a key intermediate representation to recognize indoor scenes. We frame our method as a generative probabilistic hierarchical model, where we use object category classifiers to associate low-level visual features to objects, and contextual relations to associate objects to scenes. The inherent semantic interpretation of common objects allows us to use rich sources of online data to populate the probabilistic terms of our model. In contrast to alternative computer vision based methods, we boost performance by exploiting the embedded and dynamic nature of a mobile robot. In particular, we increase detection accuracy and efficiency by using a 3D range sensor that allows us to implement a focus of attention mechanism based on geometric and structural information. Furthermore, we use concepts from information theory to propose an adaptive scheme that limits computational load by selectively guiding the search for informative objects. The operation of this scheme is facilitated by the dynamic nature of a mobile robot that is constantly changing its field of view. We test our approach using real data captured by a mobile robot navigating in Office and home environments. Our results indicate that the proposed approach outperforms several state-of-the-art techniques for scene recognition. An important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Usually localization is performed based on a purely geometric model. Through use of vision and place recognition a number of opportunities open up in terms of flexibility and association of semantics to the model. To achieve this we present an appearance based method for place recognition. The method is based on a large margin classifier in combination with a rich global image descriptor. The method is robust to variations in illumination and minor scene changes. The method is evaluated across several different cameras, changes in time-of-day and weather conditions. The results clearly demonstrate the value of the approach. The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim, this work attempts to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, it details efforts taken towards learning and generating concepts and attempts to classify places using the concepts gleaned. Several algorithms, from naive ones using only object category presence to more sophisticated ones using both objects and relationships, are proposed. Both learning and inference use the information encoded in the underlying representation-objects and relative spatial information between them. The approaches are based on learning from exemplars, clustering and the use of Bayesian network classifiers. The approaches are generative. Further, even though they are based on learning from exemplars, they are not ontology specific; i.e. they do not assume the use of any particular ontology. The presented algorithms rely on a robots inherent high-level feature extraction capability (object recognition and structural element extraction) capability to actually form concept models and infer them. Thus, this report presents methods that could enable a robot to to link sensory information to increasingly abstract concepts (spatial constructs). Such a conceptualization and the representation that results thereof would enable robots to be more cognizant of their surroundings and yet, compatible to us. Experiments on conceptualization and place classification are reported. Thus, the theme of this work is-conceptualization and classification for representation and spatial cognition. In this paper we describe the problem of Visual Place Categorization (VPC) for mobile robotics, which involves predicting the semantic category of a place from image measurements acquired from an autonomous platform. For example, a robot in an unfamiliar home environment should be able to recognize the functionality of the rooms it visits, such as kitchen, living room, etc. We describe an approach to VPC based on sequential processing of images acquired with a conventional video camera. We identify two key challenges: Dealing with non-characteristic views and integrating restricted-FOV imagery into a holistic prediction. We present a solution to VPC based upon a recently-developed visual feature known as CENTRIST (CENsus TRansform hISTogram). We describe a new dataset for VPC which we have recently collected and are making publicly available. We believe this is the first significant, realistic dataset for the VPC problem. It contains the interiors of six different homes with ground truth labels. We use this dataset to validate our solution approach, achieving promising results.