1,714 research outputs found
Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition
When a human drives a car along a road for the first time, they later
recognize where they are on the return journey typically without needing to
look in their rear-view mirror or turn around to look back, despite significant
viewpoint and appearance change. Such navigation capabilities are typically
attributed to our semantic visual understanding of the environment [1] beyond
geometry to recognizing the types of places we are passing through such as
"passing a shop on the left" or "moving through a forested area". Humans are in
effect using place categorization [2] to perform specific place recognition
even when the viewpoint is 180 degrees reversed. Recent advances in deep neural
networks have enabled high-performance semantic understanding of visual places
and scenes, opening up the possibility of emulating what humans do. In this
work, we develop a novel methodology for using the semantics-aware higher-order
layers of deep neural networks for recognizing specific places from within a
reference database. To further improve the robustness to appearance change, we
develop a descriptor normalization scheme that builds on the success of
normalization schemes for pure appearance-based techniques such as SeqSLAM [3].
Using two different datasets - one road-based, one pedestrian-based, we
evaluate the performance of the system in performing place recognition on
reverse traversals of a route with a limited field of view camera and no
turn-back-and-look behaviours, and compare to existing state-of-the-art
techniques and vanilla off-the-shelf features. The results demonstrate
significant improvements over the existing state of the art, especially for
extreme perceptual challenges that involve both great viewpoint change and
environmental appearance change. We also provide experimental analyses of the
contributions of the various system components.Comment: 9 pages, 11 figures, ICRA 201
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Visual localization enables autonomous vehicles to navigate in their
surroundings and augmented reality applications to link virtual to real worlds.
Practical visual localization approaches need to be robust to a wide variety of
viewing condition, including day-night changes, as well as weather and seasonal
variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera
pose estimates. In this paper, we introduce the first benchmark datasets
specifically designed for analyzing the impact of such factors on visual
localization. Using carefully created ground truth poses for query images taken
under a wide variety of conditions, we evaluate the impact of various factors
on 6DOF camera pose estimation accuracy through extensive experiments with
state-of-the-art localization approaches. Based on our results, we draw
conclusions about the difficulty of different conditions, showing that
long-term localization is far from solved, and propose promising avenues for
future work, including sequence-based localization approaches and the need for
better local features. Our benchmark is available at visuallocalization.net.Comment: Accepted to CVPR 2018 as a spotligh
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
Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks
Place recognition is an essential component of Simultaneous Localization And
Mapping (SLAM). Under severe appearance change, reliable place recognition is a
difficult perception task since the same place is perceptually very different
in the morning, at night, or over different seasons. This work addresses place
recognition as a domain translation task. Using a pair of coupled Generative
Adversarial Networks (GANs), we show that it is possible to generate the
appearance of one domain (such as summer) from another (such as winter) without
requiring image-to-image correspondences across the domains. Mapping between
domains is learned from sets of images in each domain without knowing the
instance-to-instance correspondence by enforcing a cyclic consistency
constraint. In the process, meaningful feature spaces are learned for each
domain, the distances in which can be used for the task of place recognition.
Experiments show that learned features correspond to visual similarity and can
be effectively used for place recognition across seasons.Comment: Accepted for publication in IEEE International Conference on Robotics
and Automation (ICRA), 201
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
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