(Georgia Institute of Technology, 2013-05)
Cunningham, Alexander; Indelman, Vadim; Dellaert, Frank
This paper presents an consistent decentralized
data fusion approach for robust multi-robot SLAM in dan-
gerous, unknown environments. The DDF-SAM 2.0 approach
extends our previous work by combining local and neigh-
borhood information in a single, consistent augmented local
map, without the overly conservative approach to avoiding
information double-counting in the previous DDF-SAM algo-
rithm. We introduce the anti-factor as a means to subtract
information in graphical SLAM systems, and illustrate its use
to both replace information in an incremental solver and to
cancel out neighborhood information from shared summarized
maps. This paper presents and compares three summarization
techniques, with two exact approaches and an approximation.
We evaluated the proposed system in a synthetic example
and show the augmented local system and the associated
summarization technique do not double-count information,
while keeping performance tractable.
(Georgia Institute of Technology, 2010)
Alcantarilla, Pablo F.; Oh, Sang Min; Mariottini, Gian Luca; Bergasa, Luis M.; Dellaert, Frank
We aim to perform robust and fast
vision-based localization using a pre-existing large
map of the scene. A key step in localization is associating the features extracted from the image with
the map elements at the current location. Although
the problem of data association has greatly benefited
from recent advances in appearance-based matching
methods, less attention has been paid to the effective
use of the geometric relations between the 3D map
and the camera in the matching process.
In this paper we propose to exploit the geometric
relationship between the 3D map and the camera
pose to determine the visibility of the features. In
our approach, we model the visibility of every map
feature w.r.t. the camera pose using a non-parametric
distribution model. We learn these non-parametric
distributions during the 3D reconstruction process,
and develop efficient algorithms to predict the visibility of features during localization. With this approach,
the matching process only uses those map features
with the highest visibility score, yielding a much
faster algorithm and superior localization results. We
demonstrate an integrated system based on the proposed idea and highlight its potential benefits for the
localization in large and cluttered environments.
(Georgia Institute of Technology, 2010)
Cunningham, Alexander; Paluri, Manohar; Dellaert, Frank
We address the problem of multi-robot distributed
SLAM with an extended Smoothing and Mapping
(SAM) approach to implement Decentralized Data Fusion
(DDF). We present DDF-SAM, a novel method for efficiently
and robustly distributing map information across
a team of robots, to achieve scalability in computational
cost and in communication bandwidth and robustness to
node failure and to changes in network topology. DDF-SAM
consists of three modules: (1) a local optimization
module to execute single-robot SAM and condense the
local graph; (2) a communication module to collect and
propagate condensed local graphs to other robots, and
(3) a neighborhood graph optimizer module to combine
local graphs into maps describing the neighborhood of a
robot. We demonstrate scalability and robustness through a
simulated example, in which inference is consistently faster
than a comparable naive approach.