15,280 research outputs found
High-Precision Localization Using Ground Texture
Location-aware applications play an increasingly critical role in everyday
life. However, satellite-based localization (e.g., GPS) has limited accuracy
and can be unusable in dense urban areas and indoors. We introduce an
image-based global localization system that is accurate to a few millimeters
and performs reliable localization both indoors and outside. The key idea is to
capture and index distinctive local keypoints in ground textures. This is based
on the observation that ground textures including wood, carpet, tile, concrete,
and asphalt may look random and homogeneous, but all contain cracks, scratches,
or unique arrangements of fibers. These imperfections are persistent, and can
serve as local features. Our system incorporates a downward-facing camera to
capture the fine texture of the ground, together with an image processing
pipeline that locates the captured texture patch in a compact database
constructed offline. We demonstrate the capability of our system to robustly,
accurately, and quickly locate test images on various types of outdoor and
indoor ground surfaces
Attention and Anticipation in Fast Visual-Inertial Navigation
We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to
estimate its state using an on-board camera and an inertial sensor, without any
prior knowledge of the external environment. We consider the case in which the
robot can allocate limited resources to VIN, due to tight computational
constraints. Therefore, we answer the following question: under limited
resources, what are the most relevant visual cues to maximize the performance
of visual-inertial navigation? Our approach has four key ingredients. First, it
is task-driven, in that the selection of the visual cues is guided by a metric
quantifying the VIN performance. Second, it exploits the notion of
anticipation, since it uses a simplified model for forward-simulation of robot
dynamics, predicting the utility of a set of visual cues over a future time
horizon. Third, it is efficient and easy to implement, since it leads to a
greedy algorithm for the selection of the most relevant visual cues. Fourth, it
provides formal performance guarantees: we leverage submodularity to prove that
the greedy selection cannot be far from the optimal (combinatorial) selection.
Simulations and real experiments on agile drones show that our approach ensures
state-of-the-art VIN performance while maintaining a lean processing time. In
the easy scenarios, our approach outperforms appearance-based feature selection
in terms of localization errors. In the most challenging scenarios, it enables
accurate visual-inertial navigation while appearance-based feature selection
fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table
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
MLPnP - A Real-Time Maximum Likelihood Solution to the Perspective-n-Point Problem
In this paper, a statistically optimal solution to the Perspective-n-Point
(PnP) problem is presented. Many solutions to the PnP problem are geometrically
optimal, but do not consider the uncertainties of the observations. In
addition, it would be desirable to have an internal estimation of the accuracy
of the estimated rotation and translation parameters of the camera pose. Thus,
we propose a novel maximum likelihood solution to the PnP problem, that
incorporates image observation uncertainties and remains real-time capable at
the same time. Further, the presented method is general, as is works with 3D
direction vectors instead of 2D image points and is thus able to cope with
arbitrary central camera models. This is achieved by projecting (and thus
reducing) the covariance matrices of the observations to the corresponding
vector tangent space.Comment: Submitted to the ISPRS congress (2016) in Prague. Oral Presentation.
Published in ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3,
131-13
Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection
This paper proposes a novel method to estimate the global scale of a 3D
reconstructed model within a Kalman filtering-based monocular SLAM algorithm.
Our Bayesian framework integrates height priors over the detected objects
belonging to a set of broad predefined classes, based on recent advances in
fast generic object detection. Each observation is produced on single frames,
so that we do not need a data association process along video frames. This is
because we associate the height priors with the image region sizes at image
places where map features projections fall within the object detection regions.
We present very promising results of this approach obtained on several
experiments with different object classes.Comment: Int. Workshop on Visual Odometry, CVPR, (July 2017
Efficient Constellation-Based Map-Merging for Semantic SLAM
Data association in SLAM is fundamentally challenging, and handling ambiguity
well is crucial to achieve robust operation in real-world environments. When
ambiguous measurements arise, conservatism often mandates that the measurement
is discarded or a new landmark is initialized rather than risking an incorrect
association. To address the inevitable `duplicate' landmarks that arise, we
present an efficient map-merging framework to detect duplicate constellations
of landmarks, providing a high-confidence loop-closure mechanism well-suited
for object-level SLAM. This approach uses an incrementally-computable
approximation of landmark uncertainty that only depends on local information in
the SLAM graph, avoiding expensive recovery of the full system covariance
matrix. This enables a search based on geometric consistency (GC) (rather than
full joint compatibility (JC)) that inexpensively reduces the search space to a
handful of `best' hypotheses. Furthermore, we reformulate the commonly-used
interpretation tree to allow for more efficient integration of clique-based
pairwise compatibility, accelerating the branch-and-bound max-cardinality
search. Our method is demonstrated to match the performance of full JC methods
at significantly-reduced computational cost, facilitating robust object-based
loop-closure over large SLAM problems.Comment: Accepted to IEEE International Conference on Robotics and Automation
(ICRA) 201
Person re-identification via efficient inference in fully connected CRF
In this paper, we address the problem of person re-identification problem,
i.e., retrieving instances from gallery which are generated by the same person
as the given probe image. This is very challenging because the person's
appearance usually undergoes significant variations due to changes in
illumination, camera angle and view, background clutter, and occlusion over the
camera network. In this paper, we assume that the matched gallery images should
not only be similar to the probe, but also be similar to each other, under
suitable metric. We express this assumption with a fully connected CRF model in
which each node corresponds to a gallery and every pair of nodes are connected
by an edge. A label variable is associated with each node to indicate whether
the corresponding image is from target person. We define unary potential for
each node using existing feature calculation and matching techniques, which
reflect the similarity between probe and gallery image, and define pairwise
potential for each edge in terms of a weighed combination of Gaussian kernels,
which encode appearance similarity between pair of gallery images. The specific
form of pairwise potential allows us to exploit an efficient inference
algorithm to calculate the marginal distribution of each label variable for
this dense connected CRF. We show the superiority of our method by applying it
to public datasets and comparing with the state of the art.Comment: 7 pages, 4 figure
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