Person:
Dellaert, Frank

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

Filters

2014 3
3
3
3
Reset filters

Settings
End date: 2014 × Start date: 2014 × search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Computing × search.filters.applied.f.resourcesubtype: Proceedings ×

Publication Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Linear-Time Estimation with Tree Assumed Density Filtering and Low-Rank Approximation
    (Georgia Institute of Technology, 2014-09) Ta, Duy-Nguyen ; Dellaert, Frank
    We present two fast and memory-efficient approximate estimation methods, targeting obstacle avoidance applications on small robot platforms. Our methods avoid a main bottleneck of traditional filtering techniques, which creates densely correlated cliques of landmarks, leading to expensive time and space complexity. We introduce a novel technique to avoid the dense cliques by sparsifying them into a tree structure and maintain that tree structure efficiently over time. Unlike other edge removal graph sparsification methods, our methods sparsify the landmark cliques by introducing new variables to de-correlate them. The first method projects the current density onto a tree rooted at the same variable at each step. The second method improves upon the first one by carefully choosing a new low-dimensional root variable at each step to replace such that the independence and conditional densities of the landmarks given the trajectory are optimally preserved. Our experiments show a significant improvement in time and space complexity of the methods compared to other standard filtering techniques in worst-case scenarios, with small trade-offs in accuracy due to low-rank approximation errors.
  • Thumbnail Image
    Item
    Modern MAP Inference Methods for Accurate and Fast Occupancy Grid Mapping on Higher Order Factor Graphs
    (Georgia Institute of Technology, 2014) Dhiman, Vikas ; Kundu, Abhijit ; Dellaert, Frank ; Corso, Jason J.
    Using the inverse sensor model has been popular in occupancy grid mapping. However, it is widely known that applying the inverse sensor model to mapping requires certain assumptions that are not necessarily true. Even the works that use forward sensor models have relied on methods like expectation maximization or Gibbs sampling which have been succeeded by more effective methods of maximum a posteriori (MAP) inference over graphical models. In this paper, we propose the use of modern MAP inference methods along with the forward sensor model. Our implementation and experimental results demonstrate that these modern inference methods deliver more accurate maps more efficiently than previously used methods.
  • Thumbnail Image
    Item
    Eliminating Conditionally Independent Sets in Factor Graphs: A Unifying Perspective based on Smart Factors
    (Georgia Institute of Technology, 2014) Carlone, Luca ; Kira, Zsolt ; Beall, Chris ; Indelman, Vadim ; Dellaert, Frank
    Factor graphs are a general estimation framework that has been widely used in computer vision and robotics. In several classes of problems a natural partition arises among variables involved in the estimation. A subset of the variables are actually of interest for the user: we call those target variables. The remaining variables are essential for the formulation of the optimization problem underlying maximum a posteriori (MAP) estimation; however these variables, that we call support variables, are not strictly required as output of the estimation problem. In this paper, we propose a systematic way to abstract support variables, defining optimization problems that are only defined over the set of target variables. This abstraction naturally leads to the definition of smart factors, which correspond to constraints among target variables. We show that this perspective unifies the treatment of heterogeneous problems, ranging from structureless bundle adjustment to robust estimation in SLAM. Moreover, it enables to exploit the underlying structure of the optimization problem and the treatment of degenerate instances, enhancing both computational efficiency and robustness.