1,076 research outputs found
LDSO: Direct Sparse Odometry with Loop Closure
In this paper we present an extension of Direct Sparse Odometry (DSO) to a
monocular visual SLAM system with loop closure detection and pose-graph
optimization (LDSO). As a direct technique, DSO can utilize any image pixel
with sufficient intensity gradient, which makes it robust even in featureless
areas. LDSO retains this robustness, while at the same time ensuring
repeatability of some of these points by favoring corner features in the
tracking frontend. This repeatability allows to reliably detect loop closure
candidates with a conventional feature-based bag-of-words (BoW) approach. Loop
closure candidates are verified geometrically and Sim(3) relative pose
constraints are estimated by jointly minimizing 2D and 3D geometric error
terms. These constraints are fused with a co-visibility graph of relative poses
extracted from DSO's sliding window optimization. Our evaluation on publicly
available datasets demonstrates that the modified point selection strategy
retains the tracking accuracy and robustness, and the integrated pose-graph
optimization significantly reduces the accumulated rotation-, translation- and
scale-drift, resulting in an overall performance comparable to state-of-the-art
feature-based systems, even without global bundle adjustment
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously
navigate between target locations quickly and reliably while avoiding obstacles
in its path, and with little to no a-priori knowledge of the operating
environment. This challenge is addressed in the present paper. We describe the
system design and software architecture of our proposed solution, and showcase
how all the distinct components can be integrated to enable smooth robot
operation. We provide critical insight on hardware and software component
selection and development, and present results from extensive experimental
testing in real-world warehouse environments. Experimental testing reveals that
our proposed solution can deliver fast and robust aerial robot autonomous
navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field
Robotic
Communication constrained cloud-based long-term visual localization in real time
Visual localization is one of the primary capabilities for mobile robots.
Long-term visual localization in real time is particularly challenging, in
which the robot is required to efficiently localize itself using visual data
where appearance may change significantly over time. In this paper, we propose
a cloud-based visual localization system targeting at long-term localization in
real time. On the robot, we employ two estimators to achieve accurate and
real-time performance. One is a sliding-window based visual inertial odometry,
which integrates constraints from consecutive observations and self-motion
measurements, as well as the constraints induced by localization on the cloud.
This estimator builds a local visual submap as the virtual observation which is
then sent to the cloud as new localization constraints. The other one is a
delayed state Extended Kalman Filter to fuse the pose of the robot localized
from the cloud, the local odometry and the high-frequency inertial
measurements. On the cloud, we propose a longer sliding-window based
localization method to aggregate the virtual observations for larger field of
view, leading to more robust alignment between virtual observations and the
map. Under this architecture, the robot can achieve drift-free and real-time
localization using onboard resources even in a network with limited bandwidth,
high latency and existence of package loss, which enables the autonomous
navigation in real-world environment. We evaluate the effectiveness of our
system on a dataset with challenging seasonal and illuminative variations. We
further validate the robustness of the system under challenging network
conditions
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