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Institute for Robotics and Intelligent Machines (IRIM)
Institute for Robotics and Intelligent Machines (IRIM)
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ItemSLAM with Object Discovery, Modeling and Mapping(Georgia Institute of Technology, 2014-09) Choudhary, Siddharth ; Trevor, Alexander J. B. ; Christensen, Henrik I. ; Dellaert, FrankObject discovery and modeling have been widely studied in the computer vision and robotics communities. SLAM approaches that make use of objects and higher level features have also recently been proposed. Using higher level features provides several benefits: these can be more discriminative, which helps data association, and can serve to inform service robotic tasks that require higher level information, such as object models and poses. We propose an approach for online object discovery and object modeling, and extend a SLAM system to utilize these discovered and modeled objects as landmarks to help localize the robot in an online manner. Such landmarks are particularly useful for detecting loop closures in larger maps. In addition to the map, our system outputs a database of detected object models for use in future SLAM or service robotic tasks. Experimental results are presented to demonstrate the approach’s ability to detect and model objects, as well as to improve SLAM results by detecting loop closures.
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ItemMobile Manipulation in Domestic Environments Using A Low Degree of Freedom Manipulator(Georgia Institute of Technology, 2012) Huckaby, Jacob ; Nieto-Granda, Carlos ; Rogers, John G. ; Trevor, Alexander J. B. ; Cosgun, Akansel ; Christensen, Henrik I.We present a mobile manipulation system used by the Georgia Tech team in the RoboCup@Home 2010 competition. An overview of the system is provided, including the approach taken for manipulation, SLAM, object detection, object recognition, and system integration. We focus on our manipulation strategy, which utilizes a low-degree of freedom manipulator and makes use of the robot’s differential drive as part of the manipulation strategy. Empirical results demonstrating our platform’s ability to detect and grasp a variety of tabletop objects are presented.
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ItemFeature-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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ItemEffects 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, VijayThis 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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ItemSlam with expectation maximization for moveable object tracking(Georgia Institute of Technology, 2010-10) Rogers, John G. ; Trevor, Alexander J. B. ; Nieto-Granda, Carlos ; Christensen, Henrik I.The goal of simultaneous localization and mapping (SLAM) is to compute the posterior distribution over landmark poses. Typically, this is made possible through the static world assumption - the landmarks remain in the same location throughout the mapping procedure. Some prior work has addressed this assumption by splitting maps into static and dynamic sets, or by recognizing moving landmarks and tracking them. In contrast to previous work, we apply an Expectation Maximization technique to a graph based SLAM approach and allow landmarks to be dynamic. The batch nature of this operation enables us to detect moveable landmarks and factor them out of the map. We demonstrate the performance of this algorithm with a series of experiments with moveable landmarks in a structured environment.
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ItemSemantic map partitioning in indoor environments using regional analysis(Georgia Institute of Technology, 2010-10) Nieto-Granda, Carlos ; Rogers, John G. ; Trevor, Alexander J. B. ; Christensen, Henrik I.Classification of spatial regions based on semantic information in an indoor environment enables robot tasks such as navigation or mobile manipulation to be spatially aware. The availability of contextual information can significantly simplify operation of a mobile platform. We present methods for automated recognition and classification of spaces into separate semantic regions and use of such information for generation of a topological map of an environment. The association of semantic labels with spatial regions is based on Human Augmented Mapping. The methods presented in this paper are evaluated both in simulation and on real data acquired from an office environment.
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ItemTables, 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.
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ItemApplying domain knowledge to slam using virtual measurements(Georgia Institute of Technology, 2010-05) Trevor, Alexander J. B. ; Rogers, John G. ; Nieto-Granda, Carlos ; Christensen, Henrik I.Simultaneous Localization and Mapping (SLAM) aims to estimate the maximum likelihood map and robot pose based on a robot’s control and sensor measurements. In structured environments, such as human environments, we might have additional domain knowledge that could be applied to produce higher quality mapping results.We present a method for using virtual measurements, which are measurements between two features in our map. To demonstrate this, we present a system that uses such virtual measurements to relate visually detected points to walls detected with a laser scanner.