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Institute for Robotics and Intelligent Machines (IRIM)

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Author: Christensen, Henrik I. × Author: Rogers, John G. × Has files: Yes × search.filters.applied.f.resourcesubtype: Post-print ×

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    Coordination Strategies for Multi-robot Exploration and Mapping
    (Georgia Institute of Technology, 2012-06) Rogers, John G. ; Nieto-Granda, Carlos ; Christensen, Henrik I.
    Situational awareness in rescue operations can be provided by teams of autonomous mobile robots. Human operators are required to teleoperate the current generation of mobile robots for this application; however, teleoperation is increasingly difficult as the number of robots is expanded. As the number of robots is increased, each robot may interfere with one another and eventually decrease mapping performance. Through careful consideration of robot team coordination and exploration strategy, large numbers of mobile robots be allocated to accomplish the mapping task more quickly and accurately.
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    Simultaneous Localization and Mapping with Learned Object Recognition and Semantic Data Association
    (Georgia Institute of Technology, 2011-09) Rogers, John G. ; Trevor, Alexander J. ; Nieto-Granda, Carlos ; Christensen, Henrik I.
    Complex and structured landmarks like objects have many advantages over low-level image features for semantic mapping. Low level features such as image corners suffer from occlusion boundaries, ambiguous data association, imaging artifacts, and viewpoint dependance. Artificial landmarks are an unsatisfactory alternative because they must be placed in the environment solely for the robot's benefit. Human environments contain many objects which can serve as suitable landmarks for robot navigation such as signs, objects, and furniture. Maps based on high level features which are identified by a learned classifier could better inform tasks such as semantic mapping and mobile manipulation. In this paper we present a technique for recognizing door signs using a learned classifier as one example of this approach, and demonstrate their use in a graphical SLAM framework with data association provided by reasoning about the semantic meaning of the sign.
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    Feature-based mapping with grounded landmark and place labels
    (Georgia Institute of Technology, 2011-06) Trevor, Alexander J. B. ; Rogers, John G. ; Nieto-Granda, Carlos ; Christensen, Henrik I.
    Service robots can benefit from maps that support their tasks and facilitate communication with humans. For efficient interaction, it is practical to be able to reference structures and objects in the environment, e.g. “fetch the mug from the kitchen table.” Towards this end, we present a feature-based SLAM and semantic mapping system which uses a variety of feature types as landmarks, including planar surfaces such as walls, tables, and shelves, as well as objects such as door signs. These landmarks can be optionally labeled by a human for later reference. Support for partitioning maps into labeled regions or places is also presented.
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    Effects of sensory precision on mobile robot localization and mapping
    (Georgia Institute of Technology, 2010-12) Rogers, John G. ; Trevor, Alexander J. B. ; Nieto-Granda, Carlos ; Cunningham, Alexander ; Paluri, Manohar ; Michael, Nathan ; Dellaert, Frank ; Christensen, Henrik I. ; Kumar, Vijay
    This paper will explore the relationship between sensory accuracy and Simultaneous Localization and Mapping (SLAM) performance. As inexpensive robots are developed with commodity components, the relationship between performance level and accuracy will need to be determined. Experiments are presented in this paper which compare various aspects of sensor performance such as maximum range, noise, angular precision, and viewable angle. In addition, mapping results from three popular laser scanners (Hokuyo’s URG and UTM30, as well as SICK’s LMS291) are compared.
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    Tables, Counters, and Shelves: Semantic Mapping of Surfaces in 3D
    (Georgia Institute of Technology, 2010-10) Trevor, Alexander J. B. ; Rogers, John G. ; Nieto-Granda, Carlos ; Christensen, Henrik I.
    Semantic mapping aims to create maps that include meaningful features, both to robots and humans. We present an extension to our feature based mapping technique that includes information about the locations of horizontal surfaces such as tables, shelves, or counters in the map. The surfaces are detected in 3D point clouds, the locations of which arc optimized by our SLAM algorithm. The resulting scans of surfaces are then analyzed to segment them into distinct surfaces, which may include measurements of a single surface across multiple scans. Preliminary results are presented in the form of a feature based map augmented with a set of 3D point clouds in a consistent global map frame that represent all detected surfaces within the mapped area.