8,263 research outputs found
Simultaneous super-resolution, tracking and mapping
This paper proposes a new visual SLAM technique that not only integrates 6DOF pose and dense structure but also simultaneously integrates the color information contained in the images over time. This involves developing an inverse model for creating a super-resolution map from many low resolution images. Contrary to classic super-resolution techniques, this is achieved here by taking into account full 3D translation and rotation within a dense localisation and mapping framework. This not only allows to take into account the full range of image deformations but also allows to propose a novel criteria for combining the low resolution images together based on the difference in resolution between different images in 6D space. Several results are given showing that this technique runs in real-time (30Hz) and is able to map large scale environments in high-resolution whilst simultaneously improving the accuracy and robustness of the tracking
Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments
We present a self-supervised approach to ignoring "distractors" in camera
images for the purposes of robustly estimating vehicle motion in cluttered
urban environments. We leverage offline multi-session mapping approaches to
automatically generate a per-pixel ephemerality mask and depth map for each
input image, which we use to train a deep convolutional network. At run-time we
use the predicted ephemerality and depth as an input to a monocular visual
odometry (VO) pipeline, using either sparse features or dense photometric
matching. Our approach yields metric-scale VO using only a single camera and
can recover the correct egomotion even when 90% of the image is obscured by
dynamic, independently moving objects. We evaluate our robust VO methods on
more than 400km of driving from the Oxford RobotCar Dataset and demonstrate
reduced odometry drift and significantly improved egomotion estimation in the
presence of large moving vehicles in urban traffic.Comment: International Conference on Robotics and Automation (ICRA), 2018.
Video summary: http://youtu.be/ebIrBn_nc-
Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer
We present a method of improving visual place recognition and metric
localisation under very strong appear- ance change. We learn an invertable
generator that can trans- form the conditions of images, e.g. from day to
night, summer to winter etc. This image transforming filter is explicitly
designed to aid and abet feature-matching using a new loss based on SURF
detector and dense descriptor maps. A network is trained to output synthetic
images optimised for feature matching given only an input RGB image, and these
generated images are used to localize the robot against a previously built map
using traditional sparse matching approaches. We benchmark our results using
multiple traversals of the Oxford RobotCar Dataset over a year-long period,
using one traversal as a map and the other to localise. We show that this
method significantly improves place recognition and localisation under changing
and adverse conditions, while reducing the number of mapping runs needed to
successfully achieve reliable localisation.Comment: Accepted at ICRA201
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
Navigation without localisation: reliable teach and repeat based on the convergence theorem
We present a novel concept for teach-and-repeat visual navigation. The
proposed concept is based on a mathematical model, which indicates that in
teach-and-repeat navigation scenarios, mobile robots do not need to perform
explicit localisation. Rather than that, a mobile robot which repeats a
previously taught path can simply `replay' the learned velocities, while using
its camera information only to correct its heading relative to the intended
path. To support our claim, we establish a position error model of a robot,
which traverses a taught path by only correcting its heading. Then, we outline
a mathematical proof which shows that this position error does not diverge over
time. Based on the insights from the model, we present a simple monocular
teach-and-repeat navigation method. The method is computationally efficient, it
does not require camera calibration, and it can learn and autonomously traverse
arbitrarily-shaped paths. In a series of experiments, we demonstrate that the
method can reliably guide mobile robots in realistic indoor and outdoor
conditions, and can cope with imperfect odometry, landmark deficiency,
illumination variations and naturally-occurring environment changes.
Furthermore, we provide the navigation system and the datasets gathered at
http://www.github.com/gestom/stroll_bearnav.Comment: The paper will be presented at IROS 2018 in Madri
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
How do computers and intelligent agents view the world around them? Feature
extraction and representation constitutes one the basic building blocks towards
answering this question. Traditionally, this has been done with carefully
engineered hand-crafted techniques such as HOG, SIFT or ORB. However, there is
no ``one size fits all'' approach that satisfies all requirements. In recent
years, the rising popularity of deep learning has resulted in a myriad of
end-to-end solutions to many computer vision problems. These approaches, while
successful, tend to lack scalability and can't easily exploit information
learned by other systems. Instead, we propose SAND features, a dedicated deep
learning solution to feature extraction capable of providing hierarchical
context information. This is achieved by employing sparse relative labels
indicating relationships of similarity/dissimilarity between image locations.
The nature of these labels results in an almost infinite set of dissimilar
examples to choose from. We demonstrate how the selection of negative examples
during training can be used to modify the feature space and vary it's
properties. To demonstrate the generality of this approach, we apply the
proposed features to a multitude of tasks, each requiring different properties.
This includes disparity estimation, semantic segmentation, self-localisation
and SLAM. In all cases, we show how incorporating SAND features results in
better or comparable results to the baseline, whilst requiring little to no
additional training. Code can be found at:
https://github.com/jspenmar/SAND_featuresComment: CVPR201
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