1,364 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
Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty
This work proposes a robust visual odometry method for structured
environments that combines point features with line and plane segments,
extracted through an RGB-D camera. Noisy depth maps are processed by a
probabilistic depth fusion framework based on Mixtures of Gaussians to denoise
and derive the depth uncertainty, which is then propagated throughout the
visual odometry pipeline. Probabilistic 3D plane and line fitting solutions are
used to model the uncertainties of the feature parameters and pose is estimated
by combining the three types of primitives based on their uncertainties.
Performance evaluation on RGB-D sequences collected in this work and two public
RGB-D datasets: TUM and ICL-NUIM show the benefit of using the proposed depth
fusion framework and combining the three feature-types, particularly in scenes
with low-textured surfaces, dynamic objects and missing depth measurements.Comment: Major update: more results, depth filter released as opensource, 34
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SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion
Active depth cameras suffer from several limitations, which cause incomplete
and noisy depth maps, and may consequently affect the performance of RGB-D
Odometry. To address this issue, this paper presents a visual odometry method
based on point and line features that leverages both measurements from a depth
sensor and depth estimates from camera motion. Depth estimates are generated
continuously by a probabilistic depth estimation framework for both types of
features to compensate for the lack of depth measurements and inaccurate
feature depth associations. The framework models explicitly the uncertainty of
triangulating depth from both point and line observations to validate and
obtain precise estimates. Furthermore, depth measurements are exploited by
propagating them through a depth map registration module and using a
frame-to-frame motion estimation method that considers 3D-to-2D and 2D-to-3D
reprojection errors, independently. Results on RGB-D sequences captured on
large indoor and outdoor scenes, where depth sensor limitations are critical,
show that the combination of depth measurements and estimates through our
approach is able to overcome the absence and inaccuracy of depth measurements.Comment: IROS 201
Monocular SLAM Supported Object Recognition
In this work, we develop a monocular SLAM-aware object recognition system
that is able to achieve considerably stronger recognition performance, as
compared to classical object recognition systems that function on a
frame-by-frame basis. By incorporating several key ideas including multi-view
object proposals and efficient feature encoding methods, our proposed system is
able to detect and robustly recognize objects in its environment using a single
RGB camera in near-constant time. Through experiments, we illustrate the
utility of using such a system to effectively detect and recognize objects,
incorporating multiple object viewpoint detections into a unified prediction
hypothesis. The performance of the proposed recognition system is evaluated on
the UW RGB-D Dataset, showing strong recognition performance and scalable
run-time performance compared to current state-of-the-art recognition systems.Comment: Accepted to appear at Robotics: Science and Systems 2015, Rome, Ital
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
Structure from Motion with Higher-level Environment Representations
Computer vision is an important area focusing on understanding,
extracting and using the information from vision-based sensor. It
has many applications such as vision-based 3D reconstruction,
simultaneous localization and mapping(SLAM) and data-driven
understanding of the real world. Vision is a fundamental sensing
modality in many different fields of application.
While the traditional structure from motion mostly uses sparse
point-based feature, this thesis aims to explore the possibility
of using higher order feature representation. It starts with a
joint work which uses straight line for feature representation
and performs bundle adjustment with straight line
parameterization. Then, we further try an even higher order
representation where we use Bezier spline for parameterization.
We start with a simple case where all contours are lying on the
plane and uses Bezier splines to parametrize the curves in the
background and optimize on both camera position and Bezier
splines. For application, we present a complete end-to-end
pipeline which produces meaningful dense 3D models from natural
data of a 3D object: the target object is placed on a structured
but unknown planar background that is modeled with splines. The
data is captured using only a hand-held monocular camera.
However, this application is limited to a planar scenario and we
manage to push the parameterizations into real 3D. Following the
potential of this idea, we introduce a more flexible higher-order
extension of points that provide a general model for structural
edges in the environment, no matter if straight or curved. Our
model relies on linked B´ezier curves, the geometric intuition
of which proves great benefits during parameter initialization
and regularization. We present the
first fully automatic pipeline that is able to generate
spline-based representations without any human supervision.
Besides a full graphical formulation of the problem, we introduce
both geometric and photometric cues as well as higher-level
concepts such overall curve visibility and viewing angle
restrictions to automatically manage the correspondences in the
graph. Results prove that curve-based structure from motion with
splines is able to outperform state-of-the-art sparse
feature-based methods, as well as to model curved edges in the
environment
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