3,907 research outputs found
Direct Monocular Odometry Using Points and Lines
Most visual odometry algorithm for a monocular camera focuses on points,
either by feature matching, or direct alignment of pixel intensity, while
ignoring a common but important geometry entity: edges. In this paper, we
propose an odometry algorithm that combines points and edges to benefit from
the advantages of both direct and feature based methods. It works better in
texture-less environments and is also more robust to lighting changes and fast
motion by increasing the convergence basin. We maintain a depth map for the
keyframe then in the tracking part, the camera pose is recovered by minimizing
both the photometric error and geometric error to the matched edge in a
probabilistic framework. In the mapping part, edge is used to speed up and
increase stereo matching accuracy. On various public datasets, our algorithm
achieves better or comparable performance than state-of-the-art monocular
odometry methods. In some challenging texture-less environments, our algorithm
reduces the state estimation error over 50%.Comment: ICRA 201
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
Efficient 2D-3D Matching for Multi-Camera Visual Localization
Visual localization, i.e., determining the position and orientation of a
vehicle with respect to a map, is a key problem in autonomous driving. We
present a multicamera visual inertial localization algorithm for large scale
environments. To efficiently and effectively match features against a pre-built
global 3D map, we propose a prioritized feature matching scheme for
multi-camera systems. In contrast to existing works, designed for monocular
cameras, we (1) tailor the prioritization function to the multi-camera setup
and (2) run feature matching and pose estimation in parallel. This
significantly accelerates the matching and pose estimation stages and allows us
to dynamically adapt the matching efforts based on the surrounding environment.
In addition, we show how pose priors can be integrated into the localization
system to increase efficiency and robustness. Finally, we extend our algorithm
by fusing the absolute pose estimates with motion estimates from a multi-camera
visual inertial odometry pipeline (VIO). This results in a system that provides
reliable and drift-less pose estimation. Extensive experiments show that our
localization runs fast and robust under varying conditions, and that our
extended algorithm enables reliable real-time pose estimation.Comment: 7 pages, 5 figure
Vision and Learning for Deliberative Monocular Cluttered Flight
Cameras provide a rich source of information while being passive, cheap and
lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work
we present the first implementation of receding horizon control, which is
widely used in ground vehicles, with monocular vision as the only sensing mode
for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a
number of contributions: novel coupling of perception and control via relevant
and diverse, multiple interpretations of the scene around the robot, leveraging
recent advances in machine learning to showcase anytime budgeted cost-sensitive
feature selection, and fast non-linear regression for monocular depth
prediction. We empirically demonstrate the efficacy of our novel pipeline via
real world experiments of more than 2 kms through dense trees with a quadrotor
built from off-the-shelf parts. Moreover our pipeline is designed to combine
information from other modalities like stereo and lidar as well if available
A bayesian approach to simultaneously recover camera pose and non-rigid shape from monocular images
© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper we bring the tools of the Simultaneous Localization and Map Building (SLAM) problem from a rigid to a deformable domain and use them to simultaneously recover the 3D shape of non-rigid surfaces and the sequence of poses of a moving camera. Under the assumption that the surface shape may be represented as a weighted sum of deformation modes, we show that the problem of estimating the modal weights along with the camera poses, can be probabilistically formulated as a maximum a posteriori estimate and solved using an iterative least squares optimization. In addition, the probabilistic formulation we propose is very general and allows introducing different constraints without requiring any extra complexity. As a proof of concept, we show that local inextensibility constraints that prevent the surface from stretching can be easily integrated.
An extensive evaluation on synthetic and real data, demonstrates that our method has several advantages over current non-rigid shape from motion approaches. In particular, we show that our solution is robust to large amounts of noise and outliers and that it does not need to track points over the whole sequence nor to use an initialization close from the ground truth.Peer ReviewedPostprint (author's final draft
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