215 research outputs found
Real Time UAV Altitude, Attitude and Motion Estimation form Hybrid Stereovision
International audienceKnowledge of altitude, attitude and motion is essential for an Unmanned Aerial Vehicle during crit- ical maneuvers such as landing and take-off. In this paper we present a hybrid stereoscopic rig composed of a fisheye and a perspective camera for vision-based navigation. In contrast to classical stereoscopic systems based on feature matching, we propose methods which avoid matching between hybrid views. A plane-sweeping approach is proposed for estimating altitude and de- tecting the ground plane. Rotation and translation are then estimated by decoupling: the fisheye camera con- tributes to evaluating attitude, while the perspective camera contributes to estimating the scale of the trans- lation. The motion can be estimated robustly at the scale, thanks to the knowledge of the altitude. We propose a robust, real-time, accurate, exclusively vision-based approach with an embedded C++ implementation. Although this approach removes the need for any non-visual sensors, it can also be coupled with an Inertial Measurement Unit
SLAM: Decentralized and Distributed Collaborative Visual-inertial SLAM System for Aerial Swarm
In recent years, aerial swarm technology has developed rapidly. In order to
accomplish a fully autonomous aerial swarm, a key technology is decentralized
and distributed collaborative SLAM (CSLAM) for aerial swarms, which estimates
the relative pose and the consistent global trajectories. In this paper, we
propose SLAM: a decentralized and distributed () collaborative SLAM
algorithm. This algorithm has high local accuracy and global consistency, and
the distributed architecture allows it to scale up. SLAM covers swarm
state estimation in two scenarios: near-field state estimation for high
real-time accuracy at close range and far-field state estimation for globally
consistent trajectories estimation at the long-range between UAVs. Distributed
optimization algorithms are adopted as the backend to achieve the goal.
SLAM is robust to transient loss of communication, network delays, and
other factors. Thanks to the flexible architecture, SLAM has the potential
of applying in various scenarios
360MonoDepth: High-Resolution 360° Monocular Depth Estimation
360{\deg} cameras can capture complete environments in a single shot, which
makes 360{\deg} imagery alluring in many computer vision tasks. However,
monocular depth estimation remains a challenge for 360{\deg} data, particularly
for high resolutions like 2K (2048x1024) and beyond that are important for
novel-view synthesis and virtual reality applications. Current CNN-based
methods do not support such high resolutions due to limited GPU memory. In this
work, we propose a flexible framework for monocular depth estimation from
high-resolution 360{\deg} images using tangent images. We project the 360{\deg}
input image onto a set of tangent planes that produce perspective views, which
are suitable for the latest, most accurate state-of-the-art perspective
monocular depth estimators. To achieve globally consistent disparity estimates,
we recombine the individual depth estimates using deformable multi-scale
alignment followed by gradient-domain blending. The result is a dense,
high-resolution 360{\deg} depth map with a high level of detail, also for
outdoor scenes which are not supported by existing methods. Our source code and
data are available at https://manurare.github.io/360monodepth/.Comment: CVPR 2022. Project page: https://manurare.github.io/360monodepth
Surround-view Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-insensitive Multi-task Framework
Surround-view fisheye perception under valet parking scenes is fundamental
and crucial in autonomous driving. Environmental conditions in parking lots
perform differently from the common public datasets, such as imperfect light
and opacity, which substantially impacts on perception performance. Most
existing networks based on public datasets may generalize suboptimal results on
these valet parking scenes, also affected by the fisheye distortion. In this
article, we introduce a new large-scale fisheye dataset called Fisheye Parking
Dataset(FPD) to promote the research in dealing with diverse real-world
surround-view parking cases. Notably, our compiled FPD exhibits excellent
characteristics for different surround-view perception tasks. In addition, we
also propose our real-time distortion-insensitive multi-task framework Fisheye
Perception Network (FPNet), which improves the surround-view fisheye BEV
perception by enhancing the fisheye distortion operation and multi-task
lightweight designs. Extensive experiments validate the effectiveness of our
approach and the dataset's exceptional generalizability.Comment: 12 pages, 11 figure
Omnidirectional Stereo Vision for Autonomous Vehicles
Environment perception with cameras is an important requirement for many applications for autonomous vehicles and robots. This work presents a stereoscopic omnidirectional camera system for autonomous vehicles which resolves the problem of a limited field of view and provides a 360° panoramic view of the environment. We present a new projection model for these cameras and show that the camera setup overcomes major drawbacks of traditional perspective cameras in many applications
Application of augmented reality and robotic technology in broadcasting: A survey
As an innovation technique, Augmented Reality (AR) has been gradually deployed in the broadcast, videography and cinematography industries. Virtual graphics generated by AR are dynamic and overlap on the surface of the environment so that the original appearance can be greatly enhanced in comparison with traditional broadcasting. In addition, AR enables broadcasters to interact with augmented virtual 3D models on a broadcasting scene in order to enhance the performance of broadcasting. Recently, advanced robotic technologies have been deployed in a camera shooting system to create a robotic cameraman so that the performance of AR broadcasting could be further improved, which is highlighted in the paper
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