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Dellaert, Frank

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End date: 2011 × Start date: 2010 × Item Type: Publication × Type: Text × search.filters.applied.f.resourcesubtype: Technical Report ×

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Now showing 1 - 5 of 5
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    Bio-Inspired Navigation
    (Georgia Institute of Technology, 2011-12) Dellaert, Frank ; Gill, Tarandeep
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    CAREER: Markov Chain Monte Carlo methods for large scale correspondence problems in computer vision and robotics
    (Georgia Institute of Technology, 2011-05-17) Dellaert, Frank ; Khan, Zia ; Potthast, Christian
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    The Bayes Tree: Enabling Incremental Reordering and Fluid Relinearization for Online Mapping
    (Georgia Institute of Technology, 2010-01-29) Kaess, Michael ; Ila, Viorela ; Roberts, Richard ; Dellaert, Frank
    In this paper we present a novel data structure, the Bayes tree, which exploits the connections between graphical model inference and sparse linear algebra. The proposed data structure provides a new perspective on an entire class of simultaneous localization and mapping (SLAM) algorithms. Similar to a junction tree, a Bayes tree encodes a factored probability density, but unlike the junction tree it is directed and maps more naturally to the square root information matrix of the SLAM problem. This makes it eminently suited to encode the sparse nature of the problem, especially in a smoothing and mapping (SAM) context. The inherent sparsity of SAM has already been exploited in the literature to produce efficient solutions in both batch and online mapping. The graphical model perspective allows us to develop a novel incremental algorithm that seamlessly incorporates reordering and relinearization. This obviates the need for expensive periodic batch operations from previous approaches, which negatively affect the performance and detract from the intended online nature of the algorithm. The new method is evaluated using simulated and real-world datasets in both landmark and pose SLAM settings.
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    Local Exponential Maps: Towards Massively Distributed Multi-robot Mapping
    (Georgia Institute of Technology, 2010) Dellaert, Frank ; Fathi, Alireza ; Cunningham, Alex ; Paluri, Balmanohar ; Ni, Kai
    We present a novel paradigm for massively distributed, large-scale multi-robot mapping. Our goal is to explore techniques that can support continuous mapping over an indefinite amount of time. We argue that to scale to city or even global scales the concept of a single globally consistent map has to be abandoned, and present an infrastructure-supported solution where most of the inference and map-maintenance is done on local "map-servers", rather than on the robot itself. The main technical contribution in the paper is a factor-graph-based scheme for making this possible, and a novel local map representation, local exponential maps, that enable indefinite map updates while remaining self-consistent over time. We present initial experimental results both in simulation and using real data, although a full-scale deployment and evaluation of the technique is left for future work.
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    EasySLAM
    (Georgia Institute of Technology, 2010) Fathi, Alireza ; Cunningham, Alex ; Paluri, Balmanohar ; Ni, Kai ; Dellaert, Frank
    EasySLAM is a robust, accurate, efficient and easy-to-use visual SLAM framework which uses the unique properties of planar landmarks to navigate robots in societal settings. Due to the use of landmarks which can be associated with semantics, a hybrid symbolic-metric SLAM variant is obtained that makes the maps immediately usable for human-robot interaction, high-level monitoring, and semantic analysis. EasySLAM associates a set of landmarks to each part of the house (e.g. kitchen, living room, bathroom, bedroom, etc.) and takes navigation commands such as "go to kitchen". Loalization and mapping, planning and navigation results are presented with an inexpensive, commercially available robot and uniquely identifiable markers. SLAM with planar landmarks is easy, robust, and fills the real need in both research and society, and we have a system that everyone can use.