(Georgia Institute of Technology, 2009-08)
Pronobis, A.; Caputo, B.; Jensfelt, Patric; Christensen, Henrik I.
An important competence for a mobile robot system is the ability to localize and
perform context interpretation. This is required to perform basic navigation and to
facilitate local specific services. Recent advances in vision have made this modality
a viable alternative to the traditional range sensors and visual place recognition
algorithms emerged as a useful and widely applied tool for obtaining information
about robot’s position. Several place recognition methods have been proposed using
vision alone or combined with sonar and/or laser. This research calls for standard
benchmark datasets for development, evaluation and comparison of solutions. To
this end, this paper presents two carefully designed and annotated image databases
augmented with an experimental procedure and extensive baseline evaluation. The
databases were gathered in an uncontrolled indoor office environment using two
mobile robots and a standard camera. The acquisition spanned across a time range
of several months and different illumination and weather conditions. Thus, the
databases are very well suited for evaluating the robustness of algorithms with
respect to a broad range of variations, often occurring in real-world settings. We
thoroughly assessed the databases with a purely appearance-based place recognition method based on Support Vector Machines and two types of rich visual features
(global and local).
(Georgia Institute of Technology, 2008-05)
Ullah, M. M.; Pronobis, A.; Caputo, B.; Luo, J.; Jensfelt, Patric; Christensen, Henrik I.
Localization and context interpretation are two
key competences for mobile robot systems. Visual place recognition,
as opposed to purely geometrical models, holds promise of
higher flexibility and association of semantics to the model. Ideally,
a place recognition algorithm should be robust to dynamic
changes and it should perform consistently when recognizing
a room (for instance a corridor) in different geographical
locations. Also, it should be able to categorize places, a crucial
capability for transfer of knowledge and continuous learning.
In order to test the suitability of visual recognition algorithms
for these tasks, this paper presents a new database, acquired in
three different labs across Europe. It contains image sequences
of several rooms under dynamic changes, acquired at the same
time with a perspective and omnidirectional camera, mounted
on a socket. We assess this new database with an appearance-based
algorithm that combines local features with support
vector machines through an ad-hoc kernel. Results show the
effectiveness of the approach and the value of the database.
(Georgia Institute of Technology, 2006-10)
Pronobis, A.; Caputo, B.; Jensfelt, Patric; Christensen, Henrik I.
An important competence for a mobile robot system
is the ability to localize and perform context interpretation.
This is required to perform basic navigation and to facilitate
local specific services. Usually localization is performed based
on a purely geometric model. Through use of vision and place
recognition a number of opportunities open up in terms of
flexibility and association of semantics to the model. To achieve
this the present paper presents an appearance based method
for place recognition. The method is based on a large margin
classifier in combination with a rich global image descriptor.
The method is robust to variations in illumination and minor
scene changes. The method is evaluated across several different
cameras, changes in time-of-day and weather conditions. The
results clearly demonstrate the value of the approach.