32,680 research outputs found
Visual Place Recognition in Changing Environments
Localization is an essential capability of mobile robots and place recognition is an important component of localization. Only having precise localization, robots can reliably plan, navigate and understand the environment around them. The main task of visual place recognition algorithms is to recognize based on the visual input if the robot has seen previously a given place in the environment. Cameras are one of the popular sensors robots get information from. They are lightweight, affordable, and provide detailed descriptions of the environment in the form of images. Cameras are shown to be useful for the vast variety of emerging applications, from virtual and augmented reality applications to autonomous cars or even fleets of autonomous cars. All these applications need precise localization. Nowadays, the state-of-the-art methods are able to reliably estimate the position of the robots using image streams. One of the big challenges still is the ability to localize a camera given an image stream in the presence of drastic visual appearance changes in the environment. Visual appearance changes may be caused by a variety of different reasons, starting from camera-related factors, such as changes in exposure time, camera position-related factors, e.g. the scene is observed from a different position or viewing angle, occlusions, as well as factors that stem from natural sources, for example seasonal changes, different weather conditions, illumination changes, etc. These effects change the way the same place in the environments appears in the image and can lead to situations where it becomes hard even for humans to recognize the places. Also, the performance of the traditional visual localization approaches, such as FABMAP or DBow, decreases dramatically in the presence of strong visual appearance changes. The techniques presented in this thesis aim at improving visual place recognition capabilities for robotic systems in the presence of dramatic visual appearance changes. To reduce the effect of visual changes on image matching performance, we exploit sequences of images rather than individual images. This becomes possible as robotic systems collect data sequentially and not in random order. We formulate the visual place recognition problem under strong appearance changes as a problem of matching image sequences collected by a robotic system at different points in time. A key insight here is the fact that matching sequences reduces the ambiguities in the data associations. This allows us to establish image correspondences between different sequences and thus recognize if two images represent the same place in the environment. To perform a search for image correspondences, we construct a graph that encodes the potential matches between the sequences and at the same time preserves the sequentiality of the data. The shortest path through such a data association graph provides the valid image correspondences between the sequences. Robots operating reliably in an environment should be able to recognize a place in an online manner and not after having recorded all data beforehand. As opposed to collecting image sequences and then determining the associations between the sequences offline, a real-world system should be able to make a decision for every incoming image. In this thesis, we therefore propose an algorithm that is able to perform visual place recognition in changing environments in an online fashion between the query and the previously recorded reference sequences. Then, for every incoming query image, our algorithm checks if the robot is in the previously seen environment, i.e. there exists a matching image in the reference sequence, as well as if the current measurement is consistent with previously obtained query images. Additionally, to be able to recognize places in an online manner, a robot needs to recognize the fact that it has left the previously mapped area as well as relocalize when it re-enters environment covered by the reference sequence. Thus, we relax the assumption that the robot should always travel within the previously mapped area and propose an improved graph-based matching procedure that allows for visual place recognition in case of partially overlapping image sequences. To achieve a long-term autonomy, we further increase the robustness of our place recognition algorithm by incorporating information from multiple image sequences, collected along different overlapping and non-overlapping routes. This allows us to grow the coverage of the environment in terms of area as well as various scene appearances. The reference dataset then contains more images to match against and this increases the probability of finding a matching image, which can lead to improved localization. To be able to deploy a robot that performs localization in large scaled environments over extended periods of time, however, collecting a reference dataset may be a tedious, resource consuming and in some cases intractable task. Avoiding an explicit map collection stage fosters faster deployment of robotic systems in the real world since no map has to be collected beforehand. By using our visual place recognition approach the map collection stage can be skipped, as we are able to incorporate the information from a publicly available source, e.g., from Google Street View, into our framework due to its general formulation. This automatically enables us to perform place recognition on already existing publicly available data and thus avoid costly mapping phase. In this thesis, we additionally show how to organize the images from the publicly available source into the sequences to perform out-of-the-box visual place recognition without previously collecting the otherwise required reference image sequences at city scale. All approaches described in this thesis have been published in peer-reviewed conference papers and journal articles. In addition to that, most of the presented contributions have been released publicly as open source software
Featureless visual processing for SLAM in changing outdoor environments
Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Many robotics applications require precise pose estimates despite operating
in large and changing environments. This can be addressed by visual
localization, using a pre-computed 3D model of the surroundings. The pose
estimation then amounts to finding correspondences between 2D keypoints in a
query image and 3D points in the model using local descriptors. However,
computational power is often limited on robotic platforms, making this task
challenging in large-scale environments. Binary feature descriptors
significantly speed up this 2D-3D matching, and have become popular in the
robotics community, but also strongly impair the robustness to perceptual
aliasing and changes in viewpoint, illumination and scene structure. In this
work, we propose to leverage recent advances in deep learning to perform an
efficient hierarchical localization. We first localize at the map level using
learned image-wide global descriptors, and subsequently estimate a precise pose
from 2D-3D matches computed in the candidate places only. This restricts the
local search and thus allows to efficiently exploit powerful non-binary
descriptors usually dismissed on resource-constrained devices. Our approach
results in state-of-the-art localization performance while running in real-time
on a popular mobile platform, enabling new prospects for robotics research.Comment: CoRL 2018 Camera-ready (fix typos and update citations
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
Long-term experiments with an adaptive spherical view representation for navigation in changing environments
Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability
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
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph
with respect to a large indoor 3D map. The contributions of this work are
three-fold. First, we develop a new large-scale visual localization method
targeted for indoor environments. The method proceeds along three steps: (i)
efficient retrieval of candidate poses that ensures scalability to large-scale
environments, (ii) pose estimation using dense matching rather than local
features to deal with textureless indoor scenes, and (iii) pose verification by
virtual view synthesis to cope with significant changes in viewpoint, scene
layout, and occluders. Second, we collect a new dataset with reference 6DoF
poses for large-scale indoor localization. Query photographs are captured by
mobile phones at a different time than the reference 3D map, thus presenting a
realistic indoor localization scenario. Third, we demonstrate that our method
significantly outperforms current state-of-the-art indoor localization
approaches on this new challenging data
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