10,323 research outputs found
A new method to determine multi-angular reflectance factor from lightweight multispectral cameras with sky sensor in a target-less workflow applicable to UAV
A new physically based method to estimate hemispheric-directional reflectance
factor (HDRF) from lightweight multispectral cameras that have a downwelling
irradiance sensor is presented. It combines radiometry with photogrammetric
computer vision to derive geometrically and radiometrically accurate data
purely from the images, without requiring reflectance targets or any other
additional information apart from the imagery. The sky sensor orientation is
initially computed using photogrammetric computer vision and revised with a
non-linear regression comprising radiometric and photogrammetry-derived
information. It works for both clear sky and overcast conditions. A
ground-based test acquisition of a Spectralon target observed from different
viewing directions and with different sun positions using a typical
multispectral sensor configuration for clear sky and overcast showed that both
the overall value and the directionality of the reflectance factor as reported
in the literature were well retrieved. An RMSE of 3% for clear sky and up to 5%
for overcast sky was observed
On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities
Current object recognition methods fail on object sets that include both
diffuse, reflective and transparent materials, although they are very common in
domestic scenarios. We show that a combination of cues from multiple sensor
modalities, including specular reflectance and unavailable depth information,
allows us to capture a larger subset of household objects by extending a state
of the art object recognition method. This leads to a significant increase in
robustness of recognition over a larger set of commonly used objects.Comment: 12 page
Vision technology/algorithms for space robotics applications
The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed
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
GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB
We address the highly challenging problem of real-time 3D hand tracking based
on a monocular RGB-only sequence. Our tracking method combines a convolutional
neural network with a kinematic 3D hand model, such that it generalizes well to
unseen data, is robust to occlusions and varying camera viewpoints, and leads
to anatomically plausible as well as temporally smooth hand motions. For
training our CNN we propose a novel approach for the synthetic generation of
training data that is based on a geometrically consistent image-to-image
translation network. To be more specific, we use a neural network that
translates synthetic images to "real" images, such that the so-generated images
follow the same statistical distribution as real-world hand images. For
training this translation network we combine an adversarial loss and a
cycle-consistency loss with a geometric consistency loss in order to preserve
geometric properties (such as hand pose) during translation. We demonstrate
that our hand tracking system outperforms the current state-of-the-art on
challenging RGB-only footage
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
We propose a real-time RGB-based pipeline for object detection and 6D pose
estimation. Our novel 3D orientation estimation is based on a variant of the
Denoising Autoencoder that is trained on simulated views of a 3D model using
Domain Randomization. This so-called Augmented Autoencoder has several
advantages over existing methods: It does not require real, pose-annotated
training data, generalizes to various test sensors and inherently handles
object and view symmetries. Instead of learning an explicit mapping from input
images to object poses, it provides an implicit representation of object
orientations defined by samples in a latent space. Our pipeline achieves
state-of-the-art performance on the T-LESS dataset both in the RGB and RGB-D
domain. We also evaluate on the LineMOD dataset where we can compete with other
synthetically trained approaches. We further increase performance by correcting
3D orientation estimates to account for perspective errors when the object
deviates from the image center and show extended results.Comment: Code available at: https://github.com/DLR-RM/AugmentedAutoencode
A Flexible Image Processing Framework for Vision-based Navigation Using Monocular Image Sensors
On-Orbit Servicing (OOS) encompasses all operations related to servicing satellites and performing other work
on-orbit, such as reduction of space debris. Servicing satellites includes repairs, refueling, attitude control and
other tasks, which may be needed to put a failed satellite back into working condition.
A servicing satellite requires accurate position and orientation (pose) information about the target spacecraft.
A large quantity of different sensor families is available to accommodate this need. However, when it comes to
minimizing mass, space and power required for a sensor system, mostly monocular imaging sensors perform very
well. A disadvantage is- when comparing to LIDAR sensors- that costly computations are needed to process the
data of the sensor.
The method presented in this paper is addressing these problems by aiming to implement three different design
principles; First: keep the computational burden as low as possible. Second: utilize different algorithms and
choose among them, depending on the situation, to retrieve the most stable results. Third: Stay modular and
flexible.
The software is designed primarily for utilization in On-Orbit Servicing tasks, where- for example- a servicer
spacecraft approaches an uncooperative client spacecraft, which can not aid in the process in any way as it is
assumed to be completely passive. Image processing is used for navigating to the client spacecraft. In this specific
scenario, it is vital to obtain accurate distance and bearing information until, in the last few meters, all six degrees
of freedom are needed to be known. The smaller the distance between the spacecraft, the more accurate pose
estimates are required.
The algorithms used here are tested and optimized on a sophisticated Rendezvous and Docking Simulation facility
(European Proximity Operations Simulator- EPOS 2.0) in its second-generation form located at the German
Space Operations Center (GSOC) in WeĂling, Germany. This particular simulation environment is real-time capable
and provides an interface to test sensor system hardware in closed loop configuration. The results from these
tests are summarized in the paper as well.
Finally, an outlook on future work is given, with the intention of providing some long-term goals as the paper is
presenting a snapshot of ongoing, by far not yet completed work. Moreover, it serves as an overview of additions
which can improve the presented method further
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