Abstract — 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. I.

Sequence Segmentation), a novel technique for place recognition and categorization from visual cues. PLISS operates on video or image streams and works by segmenting it into pieces corresponding to distinct places in the environment. An online Bayesian change-point detection framework that detects changes to model parameters is used to segment the image stream. Unlike current place recognition methods, in addition to using previously learned place models for labeling, PLISS can also detect and learn a previously unknown place or place category in an online manner. Moreover, since both the inferred boundaries of places (change-points) and the place labels are fully probabilistic, they can indicate when the inference is uncertain. New places and categories are detected using a systematic statistical hypothesis testing framework. We present extensive experiments on a large and difficult image dataset. We validate our claims by comparing results obtained using different types of features and by comparing results from PLISS against the state of the art. I.

Abstract — We address the problem of simultaneous localization and mapping by combining visual loop-closure detection with metrical information given by the robot odometry. The proposed algorithm builds in real-time topo-metric maps of an unknown environment, with a monocular or omnidirectional camera and odometry gathered by motors encoders. A dedicated improved version of our previous work on purely appearance-based loop-closure detection [1] is used to extract potential loop-closure locations. Potential locations are then verified and classified using a new validation stage. The main contributions we bring are the generalization of the validation method for the use of monocular and omnidirectional camera with the removal of the camera calibration stage, the inclusion of an odometry-based evolution model in the Bayesian filter which improves accuracy and responsiveness, and the addition of a consistent metric position estimation. This new SLAM method does not require any calibration or learning stage (i.e. no a priori information about environment). It is therefore fully incremental and generates maps usable for global localization and planned navigation. This algorithm is moreover well suited for remote processing and can be used on toy robots with very small computational power.

Abstract — Free space detection based on visual clues is an upcoming approach in robotics. Our working domain is the Virtual Rescue League of the RoboCup. In this domain efficient obstacle avoidance is crucial to find victims under challenging conditions. In this study a machine-learning approach is applied to distinguish the difference in visual appearance of obstacles and free space. Omnidirectional camera images are transformed to bird-eye view, which makes comparison with local occupancy maps possible. Bird-eye view images are automatically labeled using Laser Range information, allowing completely autonomous and continuous learning of accurate color models. Two colorbased models are compared; a Histogram Method and a Gaussian Mixture Model. Both methods achieve very good performances, with results in a high precision and recall on a typical map from the Rescue League. The Gaussian Mixture Model achieves

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We show that histograms of keypoint descriptor distances can make useful features for visual recognition. Descriptor distances are often exhaustively computed between sets of keypoints, but besides finding the k-smallest distances the structure of the distribution of these distances has been largely overlooked. We highlight the potential of such information in the task of particular scene recognition. Discriminative scene signatures in the form of histograms of keypoint descriptor distances are constructed in a supervised manner. The distances are computed between properly selected reference keypoints and the keypoints detected in the input image. The signature is low dimensional, computationally cheap to obtain, and can distinguish a large number of scenes. We introduce a scheme based on Multiclass AdaBoost to select the appropriate reference keypoints. The resulting system is capable of handling a large number of scene classes at a fraction of the time required for exhaustively matching sets of keypoints. This supports supports a coarse-to-fine search strategy for approaches reliant on keypoint matching. We test the idea on 3 datasets for particular scene recognition and report the obtained results. 1.

segmentation of indoors/outdoors sequences of spherical views

Abstract — Topological navigation consists for a robot in navigating in a topological graph which nodes are topological places. Either for indoor or outdoor environments, segmentation into topological places is a challenging issue. In this paper, we propose a common approach for indoor and outdoor environment segmentation without elaborating a complete topological navigation system. The approach is novel in that environment sensing is performed using spherical images. Environment structure estimation is performed by a global structure descriptor specially adapted to the spherical representation. This descriptor is processed by a custom designed algorithm which detects change-points defining the segmentation between topological places. I.