846 research outputs found
A Survey on Global LiDAR Localization
Knowledge about the own pose is key for all mobile robot applications. Thus
pose estimation is part of the core functionalities of mobile robots. In the
last two decades, LiDAR scanners have become a standard sensor for robot
localization and mapping. This article surveys recent progress and advances in
LiDAR-based global localization. We start with the problem formulation and
explore the application scope. We then present the methodology review covering
various global localization topics, such as maps, descriptor extraction, and
consistency checks. The contents are organized under three themes. The first is
the combination of global place retrieval and local pose estimation. Then the
second theme is upgrading single-shot measurement to sequential ones for
sequential global localization. The third theme is extending single-robot
global localization to cross-robot localization on multi-robot systems. We end
this survey with a discussion of open challenges and promising directions on
global lidar localization
AirLine: Efficient Learnable Line Detection with Local Edge Voting
Line detection is widely used in many robotic tasks such as scene
recognition, 3D reconstruction, and simultaneous localization and mapping
(SLAM). Compared to points, lines can provide both low-level and high-level
geometrical information for downstream tasks. In this paper, we propose a novel
edge-based line detection algorithm, AirLine, which can be applied to various
tasks. In contrast to existing learnable endpoint-based methods which are
sensitive to the geometrical condition of environments, AirLine can extract
line segments directly from edges, resulting in a better generalization ability
for unseen environments. Also to balance efficiency and accuracy, we introduce
a region-grow algorithm and local edge voting scheme for line parameterization.
To the best of our knowledge, AirLine is one of the first learnable edge-based
line detection methods. Our extensive experiments show that it retains
state-of-the-art-level precision yet with a 3-80 times runtime acceleration
compared to other learning-based methods, which is critical for low-power
robots
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
EVOLIN Benchmark: Evaluation of Line Detection and Association
Lines are interesting geometrical features commonly seen in indoor and urban
environments. There is missing a complete benchmark where one can evaluate
lines from a sequential stream of images in all its stages: Line detection,
Line Association and Pose error. To do so, we present a complete and exhaustive
benchmark for visual lines in a SLAM front-end, both for RGB and RGBD, by
providing a plethora of complementary metrics. We have also labelled data from
well-known SLAM datasets in order to have all in one poses and accurately
annotated lines. In particular, we have evaluated 17 line detection algorithms,
5 line associations methods and the resultant pose error for aligning a pair of
frames with several combinations of detector-association. We have packaged all
methods and evaluations metrics and made them publicly available on web-page
https://prime-slam.github.io/evolin/
Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?
International audienceAccurate visual localization is a key technology for autonomous navigation. 3D structure-based methods employ 3D models of the scene to estimate the full 6DOF pose of a camera very accurately. However, constructing (and extending) large-scale 3D models is still a significant challenge. In contrast, 2D image retrieval-based methods only require a database of geo-tagged images, which is trivial to construct and to maintain. They are often considered inaccurate since they only approximate the positions of the cameras. Yet, the exact camera pose can theoretically be recovered when enough relevant database images are retrieved. In this paper, we demonstrate experimentally that large-scale 3D models are not strictly necessary for accurate visual localization. We create reference poses for a large and challenging urban dataset. Using these poses, we show that combining image-based methods with local reconstructions results in a pose accuracy similar to the state-of-the-art structure-based methods. Our results suggest that we might want to reconsider the current approach for accurate large-scale localization
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