Person:
Dellaert, Frank

Permanent Link
https://hdl.handle.net/1853/71202
Associated Organization(s)
Organizational Unit
ORCID
ArchiveSpace Name Record

Filters

2004 - 2009 5 2010 - 2011 2
7
3 2 1 1
7
Reset filters

Settings
Has files: No × Author: Dellaert, Frank ×

Publication Search Results

Now showing 1 - 7 of 7
  • No Thumbnail Available
    Item
    iSAM2: Incremental Smoothing and Mapping with Fluid Relinearization and Incremental Variable Reordering
    (Georgia Institute of Technology, 2011) Kaess, Michael ; Johannsson, Hordur ; Roberts, Richard ; Ila, Viorela ; Leonard, John ; Dellaert, Frank
    We present iSAM2, a fully incremental, graphbased version of incremental smoothing and mapping (iSAM). iSAM2 is based on a novel graphical model-based interpretation of incremental sparse matrix factorization methods, afforded by the recently introduced Bayes tree data structure. The original iSAM algorithm incrementally maintains the square root information matrix by applying matrix factorization updates. We analyze the matrix updates as simple editing operations on the Bayes tree and the conditional densities represented by its cliques. Based on that insight, we present a new method to incrementally change the variable ordering which has a large effect on efficiency. The efficiency and accuracy of the new method is based on fluid relinearization, the concept of selectively relinearizing variables as needed. This allows us to obtain a fully incremental algorithm without any need for periodic batch steps. We analyze the properties of the resulting algorithm in detail, and show on various real and simulated datasets that the iSAM2 algorithm compares favorably with other recent mapping algorithms in both quality and efficiency.
  • No Thumbnail Available
    Item
    Visibility Learning in Large-Scale Urban Environment
    (Georgia Institute of Technology, 2011) Alcantarilla, Pablo F. ; Ni, Kai ; Bergasa, Luis M. ; Dellaert, Frank
  • No Thumbnail Available
    Item
    Rao-Blackwellized Importance Sampling of Camera Parameters from Simple User Input with Visibility Preprocessing in Line Space
    (Georgia Institute of Technology, 2006-06) Quennesson, Kevin ; Dellaert, Frank
    Users know what they see before where they are: it is more natural to talk about high level visibility information ("I see such object") than about one's location or orientation. In this paper we introduce a method to find in 3D worlds a density of viewpoints of camera locations from high level visibility constraints on objects in this world. Our method is based on Rao-Blackwellized importance sampling. For efficiency purposes, the proposal distribution used for sampling is extracted from a visibility preprocessing technique adapted from computer graphics. We apply the method for finding in a 3D city model of Atlanta the virtual locations of real-world cameras and viewpoints.
  • No Thumbnail Available
    Item
    On-line Learning of the Traversability of Unstructured Terrain for Outdoor Robot Navigation
    (Georgia Institute of Technology, 2006) Oh, Sang Min ; Rehg, James M. ; Dellaert, Frank
  • No Thumbnail Available
    Item
    Automatic Acquisition of 4D urban Models and Proactive Auditory Service for Enhanced User Experience
    (Georgia Institute of Technology, 2006) Oh, Sang Min ; Schildler, Grant ; Dellaert, Frank
  • No Thumbnail Available
    Item
    A Multifrontal QR Factorization Approach to Distributed Inference Applied to Multi-Robot Localization -and Mapping
    (Georgia Institute of Technology, 2005) Dellaert, Frank ; Krauthausen, Peter
    QR factorization is most often used as a black box algorithm, but is in fact an elegant computation on a factor graph. By computing a rooted clique tree on this graph, the computation can be parallelized across subtrees, which forms the basis of so-called multifrontal QR methods. By judiciously choosing the order in which variables are eliminated in the clique tree computation, we show that one straightforwardly obtains a method for performing inference in distributed sensor networks. One obvious application is distributed localization and mapping with a team of robots. We phrase the problem as inference on a large-scale Gaussian Markov Random Field induced by the measurement factor graph, and show how multifrontal QR on this graph solves for the global map and all the robot poses in a distributed fashion. The method is illustrated using both small and large-scale simulations, and validated in practice through actual robot experiments.
  • No Thumbnail Available
    Item
    Semantic SLAM for Collaborative Cognitive Workspaces
    (Georgia Institute of Technology, 2004) Dellaert, Frank ; Bruemmer, David