This article investigates the problem of acquiring 3D object maps of indoor household environments, in particular kitchens. The objects modeled in these maps include cupboards, tables, drawers and shelves, which are of particular importance for a household robotic assistant. Our mapping approach is based on PCD (point cloud data) representations. Sophisticated interpretation methods operating on these representations eliminate noise and resample the data without deleting the important details, and interpret the improved point clouds in terms of rectangular planes and 3D geometric shapes. We detail the steps of our mapping approach and explain the key techniques that make it work. The novel techniques include statistical analysis, persistent histogram features estimation that allows for a consistent registration, resampling with additional robust fitting techniques, and segmentation of the environment into meaningful regions. Key words: environment object model, point cloud data, geometrical reasoning 1

We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system.

The ability to represent knowledge about space and its position therein is crucial for a mobile robot. To this end, topological and semantic descriptions are gaining popularity for augmenting purely metric space representations. In this paper we present a multi-modal place classification system that allows a mobile robot to identify places and recognize semantic categories in an indoor environment. The system effectively utilizes information from different robotic sensors by fusing multiple visual cues and laser range data. This is achieved using a high-level cue integration scheme based on a Support Vector Machine (SVM) that learns how to optimally combine and weight each cue. Our multi-modal place classification approach can be used to obtain a real-time semantic space labeling system which integrates information over time and space. We perform an extensive experimental evaluation of the method for two different platforms and environments, on a realistic off-line database and in a live experiment on an autonomous robot. The results clearly demonstrate the effec-

Abstract — This article introduces a new approach to Simultaneous Localization and Mapping (SLAM) which pursues robustness and accuracy in large-scale environments. Like most successful works on SLAM, we use Bayesian filtering to provide a probabilistic estimation which can cope with uncertainty in the measurements, the robot pose, and the map. Our approach is based on the reconstruction of the robot path in a hybrid discrete-continuous state space, which naturally combines metric and topological maps. There are two fundamental characteristics that set this work apart from previous ones: (i) the use of a unified Bayesian inference approach both for the metrical and the topological parts of the problem; and (ii) the analytical formulation of belief distributions over hybrid maps, which allows us to maintain the spatial uncertainty in large spaces more accurately and efficiently than previous works. We also describe a practical implementation which aims for real-time operation. Our ideas have been validated by promising experimental results in large environments (up to 30.000 m 2, a 2Km robot path) with multiple nested loops, which could hardly be managed appropriately by other approaches. Index Terms — Bayesian filtering, hybrid metric-topological maps, loop closure, mobile robots, Rao-Blackwellized particle

A major challenge in robotics and artificial intelligence lies in creating robots that are to cooperate with people in human-populated environments, e.g. for domestic assistance or elderly care. Such robots need skills that allow them to interact with the world and the humans living and working therein. In this paper we investigate the question of spatial understanding of human-made environments. The functionalities of our system comprise perception of the world, natural language, learning, and reasoning. For this purpose we integrate state-of-the-art components from different disciplines in AI, robotics and cognitive systems into a mobile robot system. The work focuses on the description of the principles we used for the integration, including cross-modal integration, ontology-based mediation, and multiple levels of abstraction of perception. Finally, we present experiments with the integrated “CoSy Explorer ” 1 system and list some of the major lessons that were learned from its design, implementation, and evaluation.

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Abstract — This paper introduces a probabilistic, two-stage classification framework for the semantic annotation of urban maps as provided by a mobile robot. During the first stage, local scene properties are considered using a probabilistic bagof-words classifier. The second stage incorporates contextual information across a given scene via a Markov Random Field (MRF). Our approach is driven by data from an onboard camera and 3D laser scanner and uses a combination of appearancebased and geometric features. By framing the classification exercise probabilistically we are able to execute an informationtheoretic bail-out policy when evaluating appearance-based classconditional likelihoods. This efficiency, combined with low order MRFs resulting from our two-stage approach, allows us to generate scene labels at speeds suitable for online deployment and use. We demonstrate and analyze the performance of our technique on data gathered over almost 17 km of track through a city. I.

Abstract — 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 of making robots more spatially cognizant, the presented work is part of an attempt to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, this work details efforts taken towards learning and generating concepts from the perceived objects and attempts to classify places using the concepts gleaned. The approach is based on learning from exemplars, clustering and the use of Bayesian network classifiers. Experiments on conceptualization and place classification are reported. Thus, the theme of the work is- conceptualization and classification for representation and spatial cognition. I.

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Abstract — We report our first experiences with Leaving Flatland, an exploratory project that studies the key challenges of closing the loop between autonomous perception and action on challenging terrain. We propose a comprehensive system for localization, mapping, and planning for the RHex mobile robot in fully 3D indoor and outdoor environments. This system integrates Visual Odometry-based localization with new techniques in real-time 3D mapping from stereo data. The motion planner uses a new decomposition approach to adapt existing 2D planning techniques to operate in 3D terrain. We test the map-building and motion-planning subsystems on real and synthetic data, and show that they have favorable computational performance for use in high-speed autonomous navigation. I.