24,643 research outputs found
ProSLAM: Graph SLAM from a Programmer's Perspective
In this paper we present ProSLAM, a lightweight stereo visual SLAM system
designed with simplicity in mind. Our work stems from the experience gathered
by the authors while teaching SLAM to students and aims at providing a highly
modular system that can be easily implemented and understood. Rather than
focusing on the well known mathematical aspects of Stereo Visual SLAM, in this
work we highlight the data structures and the algorithmic aspects that one
needs to tackle during the design of such a system. We implemented ProSLAM
using the C++ programming language in combination with a minimal set of well
known used external libraries. In addition to an open source implementation, we
provide several code snippets that address the core aspects of our approach
directly in this paper. The results of a thorough validation performed on
standard benchmark datasets show that our approach achieves accuracy comparable
to state of the art methods, while requiring substantially less computational
resources.Comment: 8 pages, 8 figure
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
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
A comparative evaluation of interest point detectors and local descriptors for visual SLAM
Abstract In this paper we compare the behavior of different interest points detectors and descriptors under the
conditions needed to be used as landmarks in vision-based simultaneous localization and mapping (SLAM).
We evaluate the repeatability of the detectors, as well as the invariance and distinctiveness of the descriptors,
under different perceptual conditions using sequences of images representing planar objects as well as 3D scenes.
We believe that this information will be useful when selecting an appropriat
Robotic Ironing with 3D Perception and Force/Torque Feedback in Household Environments
As robotic systems become more popular in household environments, the
complexity of required tasks also increases. In this work we focus on a
domestic chore deemed dull by a majority of the population, the task of
ironing. The presented algorithm improves on the limited number of previous
works by joining 3D perception with force/torque sensing, with emphasis on
finding a practical solution with a feasible implementation in a domestic
setting. Our algorithm obtains a point cloud representation of the working
environment. From this point cloud, the garment is segmented and a custom
Wrinkleness Local Descriptor (WiLD) is computed to determine the location of
the present wrinkles. Using this descriptor, the most suitable ironing path is
computed and, based on it, the manipulation algorithm performs the
force-controlled ironing operation. Experiments have been performed with a
humanoid robot platform, proving that our algorithm is able to detect
successfully wrinkles present in garments and iteratively reduce the
wrinkleness using an unmodified iron.Comment: Accepted and to be published on the 2017 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2017) that will be held in
Vancouver, Canada, September 24-28, 201
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