7,375 research outputs found
Training a Convolutional Neural Network for Appearance-Invariant Place Recognition
Place recognition is one of the most challenging problems in computer vision,
and has become a key part in mobile robotics and autonomous driving
applications for performing loop closure in visual SLAM systems. Moreover, the
difficulty of recognizing a revisited location increases with appearance
changes caused, for instance, by weather or illumination variations, which
hinders the long-term application of such algorithms in real environments. In
this paper we present a convolutional neural network (CNN), trained for the
first time with the purpose of recognizing revisited locations under severe
appearance changes, which maps images to a low dimensional space where
Euclidean distances represent place dissimilarity. In order for the network to
learn the desired invariances, we train it with triplets of images selected
from datasets which present a challenging variability in visual appearance. The
triplets are selected in such way that two samples are from the same location
and the third one is taken from a different place. We validate our system
through extensive experimentation, where we demonstrate better performance than
state-of-art algorithms in a number of popular datasets
Deep Learning Features at Scale for Visual Place Recognition
The success of deep learning techniques in the computer vision domain has
triggered a range of initial investigations into their utility for visual place
recognition, all using generic features from networks that were trained for
other types of recognition tasks. In this paper, we train, at large scale, two
CNN architectures for the specific place recognition task and employ a
multi-scale feature encoding method to generate condition- and
viewpoint-invariant features. To enable this training to occur, we have
developed a massive Specific PlacEs Dataset (SPED) with hundreds of examples of
place appearance change at thousands of different places, as opposed to the
semantic place type datasets currently available. This new dataset enables us
to set up a training regime that interprets place recognition as a
classification problem. We comprehensively evaluate our trained networks on
several challenging benchmark place recognition datasets and demonstrate that
they achieve an average 10% increase in performance over other place
recognition algorithms and pre-trained CNNs. By analyzing the network responses
and their differences from pre-trained networks, we provide insights into what
a network learns when training for place recognition, and what these results
signify for future research in this area.Comment: 8 pages, 10 figures. Accepted by International Conference on Robotics
and Automation (ICRA) 2017. This is the submitted version. The final
published version may be slightly differen
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
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