Dellaert, Frank

Associated Organization(s)
Organizational Unit
ArchiveSpace Name Record

Publication Search Results

Now showing 1 - 10 of 17
  • Item
    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
  • Item
    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.
  • Item
    (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.
  • Item
    Probabilistic Topological Mapping for Mobile Robots using Urn Models
    (Georgia Institute of Technology, 2007) Ranganathan, Ananth ; Dellaert, Frank
    We present an application of Bayesian modeling and inference to topological mapping in robotics. This is a potentially difficult problem due to (a) the combinatorial nature of the state space, and (b) perceptual aliasing by which two different landmarks in the environment can appear similar to the robot's sensors. Hence, this presents a challenging approximate inference problem, complicated by the fact that the form of the prior on topologies is far from obvious. We deal with the latter problem by introducing the use of urn models, which very naturally encode prior assumptions in the domain of topological mapping. Secondly, we advance simulated tempering as the basis of two rapidly mixing approximate inference algorithms, based on Markov chain Monte Carlo (MCMC) and Sequential Importance Sampling (SIS), respectively. These algorithms converge quickly even though the posterior being estimated is highly peaked and multimodal. Experiments on real robots and in simulation demonstrate the efficiency and robustness of our technique.
  • Item
    Visual SLAM with a Multi-Camera Rig
    (Georgia Institute of Technology, 2006) Kaess, Michael ; Dellaert, Frank
    Camera-based simultaneous localization and mapping or visual SLAM has received much attention recently. Typically single cameras, multiple cameras in a stereo setup or omni-directional cameras are used. We propose a different approach, where multiple cameras can be mounted on a robot in an arbitrary configuration. Allowing the cameras to face in different directions yields better constraints than single cameras or stereo setups can provide, simplifying the reconstruction of large-scale environments. And in contrast to omni-directional sensors, the available resolution can be focused on areas of interest depending on the application. We describe a sparse SLAM approach that is suitable for real-time reconstruction from such multi-camera configurations. We have implemented the system and show experimental results in a large-scale environment, using a custom made eight-camera rig.
  • Item
    Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems
    (Georgia Institute of Technology, 2006) Oh, Sang Min ; Rehg, James M. ; Balch, Tucker ; Dellaert, Frank
    Switching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dynamic systems. An SLDS has significantly more descriptive power than an HMM by using continuous hidden states. However, the use of SLDS models in practical applications is challenging for several reasons. First, exact inference in SLDS models is computationally intractable. Second, the geometric duration model induced in standard SLDSs limits their representational power. Third, standard SLDSs do not provide a systematic way to robustly interpret systematic variations governed by higher order parameters. The contributions in this paper address all three challenges above. First, we present a data-driven MCMC sampling method for SLDSs as a robust and efficient approximate inference method. Second, we present segmental switching linear dynamic systems (S-SLDS), where the geometric distributions are replaced with arbitrary duration models. Third, we extend the standard model with a parametric model that can capture systematic temporal and spatial variations. The resulting parametric SLDS model (P-SLDS) uses EM to robustly interpret parametrized motions by incorporating additional global parameters that underly systematic variations of the overall motion. The overall development of the proposed inference methods and extensions for SLDSs provide a robust framework to interpret complex motions. The framework is applied to the honey bee dance interpretation task in the context of the ongoing BioTracking project at Georgia Institute of Technology. The experimental results suggest that the enhanced models provide an effective framework for a wide range of motion analysis applications.
  • Item
    A Partially Fixed Linearization Approach for Submap-Parametrized Smoothing and Mapping
    (Georgia Institute of Technology, 2005) Kipp, Alexander ; Krauthausen, Peter ; Dellaert, Frank
    We present an extension of a smoothing approach to Simultaneous Localization and Mapping (SLAM). We have previously introduced Square-Root SAM, a Smoothing and Mapping approach to SLAM based on Levenberg-Marquardt (LM) optimization. It iteratively finds the optimal nonlinear least squares solution (ML), where one iteration comprises of a linearization step, a matrix factorization, and a back-substitution step. We introduce a submap parametrization which enables a rigid transformation of parts relative to each other during the optimization process. This parameterization is used in a multifrontal QR factorization approach, in which we partially fix the linearization point for a subset of the unknowns corresponding to sub-maps. This greatly accelerates the optimization of an entire SAM graph yet yields an exact solution.
  • Item
    Exploiting Locality by Nested Dissection For Square Root Smoothing and Mapping
    (Georgia Institute of Technology, 2005) Krauthausen, Peter ; Dellaert, Frank ; Kipp, Alexander
    The problem of creating a map given only the erroneous odometry and feature measurements and locating the own position in this environment is known in the literature as the Simultaneous Localization and Mapping (SLAM) problem. In this paper we investigate how a Nested Dissection Ordering scheme can improve the the performance of a recently proposed Square Root Information Smoothing (SRIS) approach. As the SRIS does perform smoothing rather than filtering the SLAM problem becomes the Smoothing and Mapping problem (SAM). The computational complexity of the SRIS solution is dominated by the cost of transforming a matrix of all measurements into a square root form through factorization. The factorization of a fully dense measurement matrix has a cubic complexity in the worst case. We show that the computational complexity for the factorization of typical measurement matrices occurring in the SAM problem can be bound tighter under reasonable assumptions. Our work is motivated both from a numerical/linear algebra standpoint as well as by submaps used in EKF solutions to SLAM.
  • Item
    Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
    (Georgia Institute of Technology, 2005) Dellaert, Frank
    Solving the SLAM problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. We investigate smoothing approaches as a viable alternative to extended Kalman filter-based solutions to the problem. In particular, we look at approaches that factorize either the associated information matrix or the measurement matrix into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact, they can be used in either batch or incremental mode, are better equipped to deal with non-linear process and measurement models, and yield the entire robot trajectory, at lower cost. In addition, in an indirect but dramatic way, column ordering heuristics automatically exploit the locality inherent in the geographic nature of the SLAM problem. In this paper, we present the theory underlying these methods, an interpretation of factorization in terms of the graphical model associated with the SLAM problem, and simulation results that underscore the potential of these methods for use in practice.
  • Item
    A Variational inference method for Switching Linear Dynamic Systems
    (Georgia Institute of Technology, 2005) Oh, Sang Min ; Ranganathan, Ananth ; Rehg, James M. ; Dellaert, Frank
    This paper aims to present a structured variational inference algorithm for switching linear dynamical systems (SLDSs) which was initially introduced by Pavlovic and Rehg. Starting with the need for the variational approach, we proceed to the derivation of the generic (model-independent) variational update formulas which are obtained under the mean field assumption. This leads us to the derivation of an approximate variational inference algorithm for an SLDS. The details of deriving the SLDS-specific variational update equations are presented.