2,743 research outputs found
An Equivariant Observer Design for Visual Localisation and Mapping
This paper builds on recent work on Simultaneous Localisation and Mapping
(SLAM) in the non-linear observer community, by framing the visual localisation
and mapping problem as a continuous-time equivariant observer design problem on
the symmetry group of a kinematic system. The state-space is a quotient of the
robot pose expressed on SE(3) and multiple copies of real projective space,
used to represent both points in space and bearings in a single unified
framework. An observer with decoupled Riccati-gains for each landmark is
derived and we show that its error system is almost globally asymptotically
stable and exponentially stable in-the-large.Comment: 12 pages, 2 figures, published in 2019 IEEE CD
Visual SLAM for flying vehicles
The ability to learn a map of the environment is important for numerous types of robotic vehicles. In this paper, we address the problem of learning a visual map of the ground using flying vehicles. We assume that the vehicles are equipped with one or two low-cost downlooking cameras in combination with an attitude sensor. Our approach is able to construct a visual map that can later on be used for navigation. Key advantages of our approach are that it is comparably easy to implement, can robustly deal with noisy camera images, and can operate either with a monocular camera or a stereo camera system. Our technique uses visual features and estimates the correspondences between features using a variant of the progressive sample consensus (PROSAC) algorithm. This allows our approach to extract spatial constraints between camera poses that can then be used to address the simultaneous localization and mapping (SLAM) problem by applying graph methods. Furthermore, we address the problem of efficiently identifying loop closures. We performed several experiments with flying vehicles that demonstrate that our method is able to construct maps of large outdoor and indoor environments. © 2008 IEEE
3D Reconstruction & Assessment Framework based on affordable 2D Lidar
Lidar is extensively used in the industry and mass-market. Due to its
measurement accuracy and insensitivity to illumination compared to cameras, It
is applied onto a broad range of applications, like geodetic engineering, self
driving cars or virtual reality. But the 3D Lidar with multi-beam is very
expensive, and the massive measurements data can not be fully leveraged on some
constrained platforms. The purpose of this paper is to explore the possibility
of using cheap 2D Lidar off-the-shelf, to preform complex 3D Reconstruction,
moreover, the generated 3D map quality is evaluated by our proposed metrics at
the end. The 3D map is constructed in two ways, one way in which the scan is
performed at known positions with an external rotary axis at another plane. The
other way, in which the 2D Lidar for mapping and another 2D Lidar for
localization are placed on a trolley, the trolley is pushed on the ground
arbitrarily. The generated maps by different approaches are converted to
octomaps uniformly before the evaluation. The similarity and difference between
two maps will be evaluated by the proposed metrics thoroughly. The whole
mapping system is composed of several modular components. A 3D bracket was made
for assembling of the Lidar with a long range, the driver and the motor
together. A cover platform made for the IMU and 2D Lidar with a shorter range
but high accuracy. The software is stacked up in different ROS packages.Comment: 7 pages, 9 Postscript figures. Accepted by 2018 IEEE International
Conference on Advanced Intelligent Mechatronic
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
RT-SLAM: A Generic and Real-Time Visual SLAM Implementation
This article presents a new open-source C++ implementation to solve the SLAM
problem, which is focused on genericity, versatility and high execution speed.
It is based on an original object oriented architecture, that allows the
combination of numerous sensors and landmark types, and the integration of
various approaches proposed in the literature. The system capacities are
illustrated by the presentation of an inertial/vision SLAM approach, for which
several improvements over existing methods have been introduced, and that copes
with very high dynamic motions. Results with a hand-held camera are presented.Comment: 10 page
Visual-Inertial Mapping with Non-Linear Factor Recovery
Cameras and inertial measurement units are complementary sensors for
ego-motion estimation and environment mapping. Their combination makes
visual-inertial odometry (VIO) systems more accurate and robust. For globally
consistent mapping, however, combining visual and inertial information is not
straightforward. To estimate the motion and geometry with a set of images large
baselines are required. Because of that, most systems operate on keyframes that
have large time intervals between each other. Inertial data on the other hand
quickly degrades with the duration of the intervals and after several seconds
of integration, it typically contains only little useful information.
In this paper, we propose to extract relevant information for visual-inertial
mapping from visual-inertial odometry using non-linear factor recovery. We
reconstruct a set of non-linear factors that make an optimal approximation of
the information on the trajectory accumulated by VIO. To obtain a globally
consistent map we combine these factors with loop-closing constraints using
bundle adjustment. The VIO factors make the roll and pitch angles of the global
map observable, and improve the robustness and the accuracy of the mapping. In
experiments on a public benchmark, we demonstrate superior performance of our
method over the state-of-the-art approaches
Interest point detectors for visual SLAM
In this paper we present several interest points detectors and we analyze their suitability when used as landmark extractors for vision-based simultaneous localization and mapping (vSLAM). For this purpose, we evaluate the detectors according to their repeatability under changes in viewpoint and scale. These are the desired requirements for visual landmarks. Several experiments were carried out using sequence of images captured with high precision. The sequences represent planar objects as well as 3D scenes
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
- …