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chap5.bib
@INPROCEEDINGS{Bertolli06a,
AUTHOR = {Federico Bertolli and Patric Jensfelt and Henrik I. Christensen},
TITLE = {SLAM using Visual Scan-Matching with Distinguishable {3D} Points},
BOOKTITLE = {Proceedings of the International Conference on Intelligent Robots
and Systems (IROS'06)},
YEAR = {2006},
ABSTRACT = {Scan-matching based on data from a laser scanner is frequently used
for mapping and localization. This paper presents an scan-matching
approach based instead on visual information from a stereo system.
The Scale Invariant Feature Transform (SIFT) is used together with
epipolar constraints to get high matching precision between the stereo
images. Calculating the 3D position of the corresponding points in
the world results in a visual scan where each point has a descriptor
attached to it. These descriptors can be used when matching scans
acquired from different positions. Just like in the work with laser
based scan matching a map can be defined as a set of reference scans
and their corresponding acquisition point. In essence this reduces
each visual scan that can consist of hundreds of points to a single
entity for which only the corresponding robot pose has to be estimated
in the map. This reduces the overall complexity of the map. The SIFT
descriptor attached to each of the points in the reference allows
for robust matching and detection of loop closing situations. The
paper presents real-world experimental results from an indoor office
environment.},
URL = {http://www.cognitivesystems.org/publications/fedepaper.pdf}
}
@ARTICLE{Folkesson07a,
AUTHOR = {John Folkesson and Patric Jensfelt and Henrik Christensen},
TITLE = {The M-space Feature Representation for SLAM},
JOURNAL = {IEEE Transactions on Robotics},
YEAR = {2007},
VOLUME = {23},
PAGES = {1024--1035},
NUMBER = {5},
MONTH = OCT,
ABSTRACT = {In this paper a new feature representation for Simultaneous Localization
and Mapping (SLAM) is discussed. The representation addresses feature
symmetries and constraints explicitly to make the basic model numerically
robust. In previous SLAM work, complete initialization of features
is typically performed prior to introduction of a new feature into
the map. This results in delayed use of new data. To allow early
use of sensory data the new feature representation addresses the
use of features that initially have been partially observed. This
is achieved by explicitly modelling the sub-space of a feature that
has been observed. In addition to accounting for the special properties
of each feature type, the commonalities can be exploited in the new
representation to create a feature framework that allows for interchanging
of SLAM algorithms, sensor and features. Experimental results are
presented using a low-cost web-cam, a laser range scanner and combinations
thereof. },
URL = {http://www.cognitivesystems.org/publications/mspace.pdf}
}
@INPROCEEDINGS{Frintrop08a,
AUTHOR = {Simone Frintrop and Patric Jensfelt},
TITLE = {Active Gaze Control for Attentional Visual {SLAM}},
BOOKTITLE = {Proceedings of the International Conference on Robotics and Automation
(ICRA'08)},
YEAR = {2008},
ABSTRACT = {In this paper, we introduce an approach to active camera control for
visual SLAM. Features, detected by a biologically motivated attention
system, are tracked over several frames to determine stable landmarks.
Matching of features to database entries enables global loop closing.
The focus of this paper is the active camera control module, which
supports the system with three behaviours: i) A tracking behaviour
tracks promising landmarks and prevents them from leaving the field
of view. ii) A redetection behaviour directs the camera actively
to regions where landmarks are expected and thus supports loop closing.
iii) Finally, an exploration behaviour investigates regions without
landmarks and enables a more uniform distribution of landmarks. Several
real-world experiments show that the active camera control outperforms
the passive system considerably. },
URL = {http://www.cognitivesystems.org/publications/frintropJensfelt_ICRA2008.pdf}
}
@INPROCEEDINGS{Frintrop08b,
AUTHOR = {Simone Frintrop and Patric Jensfelt},
TITLE = {Attentional Landmarks and Active Gaze Control for Visual {SLAM}},
BOOKTITLE = {IEEE Transactions on Robotics, special Issue on Visual {SLAM}},
YEAR = {2008},
VOLUME = {24},
MONTH = OCT,
ABSTRACT = {This paper is centered around landmark detection, tracking and matching
for visual SLAM (Simultaneous Localization And Mapping) using a monocular
vision system with active gaze control. We present a system specialized
in creating and maintaining a sparse set of landmarks based on a
biologically motivated feature selection strategy. A visual attention
system detects salient features which are highly discriminative,
ideal candidates for visual landmarks which are easy to redetect.
Features are tracked over several frames to determine stable landmarks
and to estimate their 3D position in the environment. Matching of
current landmarks to database entries enables loop closing. Active
gaze control allows us to overcome some of the limitations of using
a monocular vision system with a relatively small field of view.
It supports (i) the tracking of landmarks which enable a better position
estimation, (ii) the exploration of regions without landmarks to
obtain a better distribution of landmarks in the environment, and
(iii) the active redetection of landmarks to enable loop closing
in situations in which a fixed camera fails to close the loop. Several
real-world experiments show that accurate position estimation is
obtained with the presented system and that active camera control
outperforms the passive approach. },
URL = {http://www.cognitivesystems.org/publications/frintropJensfeltTRO2008.pdf}
}
@ARTICLE{kruijff07jars,
AUTHOR = {Geert-Jan M. Kruijff and Hendrik Zender and Patric Jensfelt and Henrik
I. Christensen},
TITLE = {Situated Dialogue and Spatial Organization: What, Where\ldots and
Why?},
JOURNAL = {International Journal of Advanced Robotic Systems, Special Issue
on Human-Robot Interaction},
YEAR = {2007},
VOLUME = {4},
PAGES = {125--138},
NUMBER = {1},
MONTH = {March},
ABSTRACT = {The paper presents an HRI architecture for human-augmented mapping,
which has been implemented and tested on an autonomous mobile robotic
platform. Through interaction with a human, the robot can augment
its autonomously acquired metric map with qualitative information
about locations and objects in the environment. The system implements
various interaction strategies observed in independently performed
Wizard-of-Oz studies. The paper discusses an ontology-based approach
to multi-layered conceptual spatial mapping that provides a common
ground for human-robot dialogue. This is achieved by combining acquired
knowledge with innate conceptual commonsense knowledge in order to
infer new knowledge. The architecture bridges the gap between the
rich semantic representations of the meaning expressed by verbal
utterances on the one hand and the robot's internal sensor-based
world representation on the other. It is thus possible to establish
references to spatial areas in a situated dialogue between a human
and a robot about their environment. The resulting conceptual descriptions
represent qualitative knowledge about locations in the environment
that can serve as a basis for achieving a notion of situational awareness.},
URL = {http://www.cognitivesystems.org/publications/kruijff_etal07-jars.pdf}
}
@INPROCEEDINGS{Galvez08a,
AUTHOR = {Dorian G\'alvez L\'opez and Kristoffer Sj\"{o} and Chandana Paul
and Patric Jensfelt},
TITLE = {Hybrid Laser and Vision Based Object Search and Localization},
BOOKTITLE = {Proceedings of the International Conference on Robotics and Automation
(ICRA'08)},
YEAR = {2008},
ABSTRACT = {We describe a method for an autonomous robot to efficiently locate
one or more distinct objects in a realistic environment using monocular
vision. We demonstra te how to efficiently subdivide acquired images
into interest regions for the robot to zoom in on, using receptive
field cooccurrence histograms. Objects are recognized through SIFT
feature matching and the positions of the objects are es timated.
Assuming a 2D map of the robot's surroundings and a set of navigation
nodes betw een which it is free to move, we show how to compute an
efficient sensing plan that allows the robot's camera to cover the
environment, while obeying restrictions on the different objects'
maximum and minimum viewing distances. The approach has been implemented
on a real robotic system and results are prese nted showing its practicability
and the quality of the position estimates obtained.},
URL = {http://www.cognitivesystems.org/publications/galvezetal-icra08.pdf}
}
@INPROCEEDINGS{luo07iros,
AUTHOR = {Luo, J. and Pronobis, A. and Caputo, B. and Jensfelt, P.},
TITLE = {Incremental Learning for Place Recognition in Dynamic Environments},
BOOKTITLE = {Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS'07)},
YEAR = {2007},
ADDRESS = {San Diego, CA, USA},
MONTH = {October},
ABSTRACT = {Vision-based place recognition is a desirable feature for an autonomous
mobile system. In order to work in realistic scenarios, visual recognition
algorithms should be adaptive, i.e. should be able to learn from
experience and adapt continuously to changes in the environment.
This paper presents a discriminative incremental learning approach
to place recognition. We use a recently introduced version of the
incremental SVM, which allows to control the memory requirements
as the system updates its internal representation. At the same time,
it preserves the recognition performance of the batch algorithm.
In order to assess the method, we acquired a database capturing the
intrinsic variability of places over time. Extensive experiments
show the power and the potential of the approach.},
URL = {http://www.cognitivesystems.org/publications/luo07iros.pdf}
}
@TECHREPORT{luo06kth,
AUTHOR = {Luo, J. and Pronobis, A. and Caputo, B. and Jensfelt, P.},
TITLE = {The {KTH-IDOL2} Database},
INSTITUTION = {Kungliga Tekniska Hoegskolan, CVAP/CAS},
YEAR = {2006},
NUMBER = {CVAP304},
MONTH = {October},
URL = {http://www.cognitivesystems.org/publications/luo06kth_idol2.pdf}
}
@ARTICLE{MartinezMozos07a,
AUTHOR = {Oscar Mart\'{i}nez Mozos and Rudolph Triebel and Patric Jensfelt
and Axel Rottmann and Wolfram Burgard},
TITLE = {Supervised Semantic Labeling of Places using Information Extracted
from Laser and Vision Sensor Data},
JOURNAL = {Robotics and Autonomous Systems Journal},
YEAR = {2007},
VOLUME = {55},
PAGES = {391--402},
NUMBER = {5},
MONTH = MAY,
ABSTRACT = {Indoor environments can typically be divided into places with different
functionalities like corridors, rooms or doorways. The ability to
learn such semantic categories from sensor data enables a mobile
robot to extend the representation of the environment facilitating
the interaction with humans. As an example, natural language terms
like ``corridor" or ``room" can be used to communicate the position
of the robot in a map in a more intuitive way. In this work, we first
propose an approach based on supervised learning to classify the
pose of a mobile robot into semantic classes. Our method uses AdaBoost
to boost simple features extracted from sensor range data into a
strong classifier. We present two main applications of this approach.
Firstly, we show how our approach can be utilized by a moving robot
for an online classification of the poses traversed along its path
using a hidden Markov model. In this case we additionally use as
features objects extracted from images. Secondly, we introduce an
approach to learn topological maps from geometric maps by applying
our semantic classification procedure in combination with a probabilistic
relaxation method. Alternatively, we apply associative Markov networks
to classify geometric maps and compare the results with the relaxation
approach. Experimental results obtained in simulation and with real
robots demonstrate the effectiveness of our approach in various indoor
environments. },
URL = {http://www.cognitivesystems.org/publications/mozos2007RAS.pdf}
}
@INPROCEEDINGS{pronobis07iros,
AUTHOR = {Pronobis, A. and Caputo, B.},
TITLE = {Confidence-based Cue Integration for Visual Place Recognition},
BOOKTITLE = {Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS'07)},
YEAR = {2007},
ADDRESS = {San Diego, CA, USA},
MONTH = {October},
ABSTRACT = {A distinctive feature of intelligent systems is their capability to
analyze their level of expertise for a given task; in other words,
they know what they know. As a way towards this ambitious goal, this
paper presents a recognition algorithm able to measure its own level
of confidence and, in case of uncertainty, to seek for extra information
so to increase its own knowledge and ultimately achieve better performance.
We focus on the visual place recognition problem for topological
localization, and we take an SVM approach. We propose a new method
for measuring the confidence level of the classification output,
based on the distance of a test image and the average distance of
training vectors. This method is combined with a discriminative accumulation
scheme for cue integration. We show with extensive experiments that
the resulting algorithm achieves better performances for two visual
cues than the classic single cue SVM on the same task, while minimising
the computational load. More important, our method provides a reliable
measure of the level of confidence of the decision.},
URL = {http://www.cognitivesystems.org/publications/pronobis07iros.pdf}
}
@TECHREPORT{pronobis06idiap,
AUTHOR = {Pronobis, A. and Caputo, B.},
TITLE = {The More You Learn, the Less You Store: Memory-Controlled Incremental
{SVM}},
INSTITUTION = {IDIAP},
YEAR = {2006},
TYPE = {IDIAP-RR},
NUMBER = {51},
ABSTRACT = {The capability to learn from experience is a key property for a visual
recognition algorithm working in realistic settings. This paper presents
an SVM-based algorithm, capable of learning model representations
incrementally while keeping under control memory requirements. We
combine an incremental extension of SVMs with a method reducing the
number of support vectors needed to build the decision function without
any loss in performance, introducing a parameter which permits a
user-set trade-off between performance and memory. The resulting
algorithm is guaranteed to achieve the same recognition results as
the original incremental method while reducing the memory growth.
Moreover, experiments in two domains of material and place recognition
show the possibility of a consistent reduction of memory requirements
with only a moderate loss in performance. For example, results show
that when the user accepts a reduction in recognition rate of 5%,
this yields a memory reduction of up to 50%.},
URL = {http://www.cognitivesystems.org/publications/pronobis06idiap.pdf}
}
@INPROCEEDINGS{pronobis06iros,
AUTHOR = {Pronobis, A. and Caputo, B. and Jensfelt, P. and Christensen, H.
I.},
TITLE = {A Discriminative Approach to Robust Visual Place Recognition},
BOOKTITLE = {Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS'06)},
YEAR = {2006},
ADDRESS = {Beijing, China},
MONTH = {October},
ABSTRACT = {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.},
URL = {http://www.cognitivesystems.org/publications/pronobis06iros.pdf}
}
@INPROCEEDINGS{pronobis08icra,
AUTHOR = {Pronobis, A. and Mart\'{i}nez Mozos, O. and Caputo, B.},
TITLE = {{SVM}-based Discriminative Accumulation Scheme for Place Recognition},
BOOKTITLE = {Proceedings of the IEEE International Conference on Robotics and
Automation (ICRA'08)},
YEAR = {2008},
ADDRESS = {Pasadena, CA, USA},
MONTH = {May},
ABSTRACT = {Integrating information coming from different sensors is a fundamental
capability for autonomous robots. For complex tasks like topological
localization, it would be desirable to use multiple cues, possibly
from different modalities, so to achieve robust performance. This
paper proposes a new method for integrating multiple cues. For each
cue we train a large margin classifier which outputs a set of scores
indicating the confidence of the decision. These scores are then
used as input to a Support Vector Machine, that learns how to weight
each cue, for each class, optimally during training. We call this
algorithm SVM-based Discriminative Accumulation Scheme (SVM-DAS).
We applied our method to the topological localization task, using
vision and laser-based cues. Experimental results clearly show the
value of our approach.},
URL = {http://www.cognitivesystems.org/publications/pronobis08icra.pdf}
}
@INPROCEEDINGS{rottmann05aaai,
AUTHOR = {Axel Rottmann and Oscar Martinez Mozos and Cyrill Stachniss and Wolfram
Burgard},
TITLE = {Place Classification of Indoor Environments with Mobile Robots using
Boosting},
BOOKTITLE = {Proceedings of the National Conference on Artificial Intelligence},
YEAR = {2005},
PAGES = {1306-1311},
ADDRESS = {Pittsburgh, PA, USA},
ABSTRACT = {Indoor environments can typically be divided into places with different
functionalities like kitchens, offices, or seminar rooms. We believe
that such semantic information enables a mobile robot to more efficiently
accomplish a variety of tasks such as human-robot interaction, path-planning,
or localization. This paper presents a supervised learning approach
to label different locations using boosting. We train a classifier
using features extracted from vision and laser range data. Furthermore,
we apply a Hidden Markov Model to increase the robustness of the
final classification. Our technique has been implemented and tested
on real robots as well as in simulation. The experiments demonstrate
that our approach can be utilized to robustly classify places into
semantic categories. We also present an example of localization using
semantic labeling.},
URL = {http://www.cognitivesystems.org/publications/rottmann2005aaai.pdf}
}
@INPROCEEDINGS{Sjoe08b,
AUTHOR = {Kristoffer Sj\"o and Chandana Paul},
TITLE = {Object Localization using Bearing Only Visual Detection},
BOOKTITLE = {Proceedings of the 10th International Conference on Intelligent Autonomous
Systems},
YEAR = {2008},
MONTH = {july},
ABSTRACT = {This work demonstrates how an autonomous robotic platform can use
intrinsically noisy, coarse-scale visual methods lacking range information
to produce good estimates of the location of objects, by using a
map space representation for weighting together multiple observations
from different vantage points. As the robot moves through the environment
it acquires visual images which are processed by means of a fast
but noisy visual detection algorithm that gives bearing only information.
The results from the detection are then projected from image space
into map space, where data from multiple viewpoints can intrinsically
combine to yield an increasingly accurate picture of the location
of objects. This method has been implemented and shown to work for
object localization on a real robot. It has also been tested extensively
in simulation, with systematically varied false positive and false
negative detection rates. The results demonstrate that this is a
viable method for object localization, even under a wide range of
sensor uncertainties.},
URL = {http://www.cognitivesystems.org/publications/SjooIAS08.pdf}
}
@TECHREPORT{ullah07cold,
AUTHOR = {Ullah, M. M. and Pronobis, A. and Caputo, B. and Luo, J. and Jensfelt,
P.},
TITLE = {The {COLD} Database},
INSTITUTION = {Kungliga Tekniska Hoegskolan, CVAP/CAS},
YEAR = {2007},
NUMBER = {TRITA-CSC-CV 2007:1},
MONTH = {October},
URL = {http://www.cognitivesystems.org/publications/ullah07cold.pdf}
}
@INPROCEEDINGS{ullah08icra,
AUTHOR = {Ullah, M. M. and Pronobis, A. and Caputo, B. and Luo, J. and Jensfelt,
P. and Christensen, H. I.},
TITLE = {Towards Robust Place Recognition for Robot Localization},
BOOKTITLE = {Proceedings of the IEEE International Conference on Robotics and
Automation (ICRA'08)},
YEAR = {2008},
ADDRESS = {Pasadena, CA, USA},
MONTH = {May},
ABSTRACT = {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.},
URL = {http://www.cognitivesystems.org/publications/ullah08icra.pdf}
}
@ARTICLE{zender08ras_fs2hsc,
AUTHOR = {Hendrik Zender and \'{O}scar Mart\'{i}nez Mozos and Patric Jensfelt
and Geert-Jan M. Kruijff and Wolfram Burgard},
TITLE = {Conceptual Spatial Representations for Indoor Mobile Robots},
JOURNAL = {Robotics and Autonomous Systems},
YEAR = {2008},
VOLUME = {56},
PAGES = {493--502},
NUMBER = {6},
MONTH = {June},
ABSTRACT = {We present an approach for creating conceptual representations of
human-made indoor environments using mobile robots. The concepts
refer to spatial and functional properties of typical indoor environments.
Following findings in cognitive psychology, our model is composed
of layers representing maps at different levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser
and vision sensors for place and ob ject recognition. The system
also incorporates a linguistic framework that actively supports the
map acquisition process, and which is used for situated dialogue.
Finally, we discuss the capabilities of the integrated system.},
DOI = {10.1016/j.robot.2008.03.007},
PUBLISHER = {Elsevier},
URL = {http://www.cognitivesystems.org/publications/zender_etal08-ras-aam.pdf}
}
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