15,952 research outputs found
Learning Matchable Image Transformations for Long-term Metric Visual Localization
Long-term metric self-localization is an essential capability of autonomous
mobile robots, but remains challenging for vision-based systems due to
appearance changes caused by lighting, weather, or seasonal variations. While
experience-based mapping has proven to be an effective technique for bridging
the `appearance gap,' the number of experiences required for reliable metric
localization over days or months can be very large, and methods for reducing
the necessary number of experiences are needed for this approach to scale.
Taking inspiration from color constancy theory, we learn a nonlinear
RGB-to-grayscale mapping that explicitly maximizes the number of inlier feature
matches for images captured under different lighting and weather conditions,
and use it as a pre-processing step in a conventional single-experience
localization pipeline to improve its robustness to appearance change. We train
this mapping by approximating the target non-differentiable localization
pipeline with a deep neural network, and find that incorporating a learned
low-dimensional context feature can further improve cross-appearance feature
matching. Using synthetic and real-world datasets, we demonstrate substantial
improvements in localization performance across day-night cycles, enabling
continuous metric localization over a 30-hour period using a single mapping
experience, and allowing experience-based localization to scale to long
deployments with dramatically reduced data requirements.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the
IEEE International Conference on Robotics and Automation (ICRA'20), Paris,
France, May 31-June 4, 202
Fluorescence Excitation by Enhanced Plasmon Upconversion under Continuous Wave Illumination
We demonstrate effective background-free continuous wave nonlinear optical
excitation of molecules that are sandwiched between asymmetrically constructed
plasmonic gold nanoparticle clusters. We observe that near infrared photons are
converted to visible photons through efficient plasmonic second harmonic
generation. Our theoretical model and simulations demonstrate that Fano
resonances may be responsible for being able to observe nonlinear conversion
using a continuous wave light source. We show that nonlinearity enhancement of
plasmonic nanostructures via coupled quantum mechanical oscillators such as
molecules can be several orders larger as compared to their classical
counterparts.Comment: 11 pages, 8 figure
Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer
We present a method of improving visual place recognition and metric
localisation under very strong appear- ance change. We learn an invertable
generator that can trans- form the conditions of images, e.g. from day to
night, summer to winter etc. This image transforming filter is explicitly
designed to aid and abet feature-matching using a new loss based on SURF
detector and dense descriptor maps. A network is trained to output synthetic
images optimised for feature matching given only an input RGB image, and these
generated images are used to localize the robot against a previously built map
using traditional sparse matching approaches. We benchmark our results using
multiple traversals of the Oxford RobotCar Dataset over a year-long period,
using one traversal as a map and the other to localise. We show that this
method significantly improves place recognition and localisation under changing
and adverse conditions, while reducing the number of mapping runs needed to
successfully achieve reliable localisation.Comment: Accepted at ICRA201
Evaluation of the usefulness of various simulation technology options for TERPS enhancement
Current approved terminal instrument procedures (TERPS) do not permit the full exploitation of the helicopter's unique flying characteristics. Enhanced TERPS need to be developed for a host of non-standard landing sites and navigation aids. Precision navigation systems such as microwave landing systems (MLS) and the Global Positioning System (GPS) open the possibility of curved paths, steep glide slopes, and decelerating helicopter approaches. This study evaluated the feasibility, benefits, and liabilities of using helicopter cockpit simulators in place of flight testing to develop enhanced TERPS criteria for non-standard flight profiles and navigation equipment. Near-term (2 to 5 year) requirements for conducting simulator studies to verify that they produce suitable data comparable to that obtained from previous flight tests are discussed. The long-term (5 to 10 year) research and development requirements to provide necessary modeling for continued simulator-based testing to develop enhanced TERPS criteria are also outlined
Multi-Modal Enhancement Techniques for Visibility Improvement of Digital Images
Image enhancement techniques for visibility improvement of 8-bit color digital images based on spatial domain, wavelet transform domain, and multiple image fusion approaches are investigated in this dissertation research.
In the category of spatial domain approach, two enhancement algorithms are developed to deal with problems associated with images captured from scenes with high dynamic ranges. The first technique is based on an illuminance-reflectance (I-R) model of the scene irradiance. The dynamic range compression of the input image is achieved by a nonlinear transformation of the estimated illuminance based on a windowed inverse sigmoid transfer function. A single-scale neighborhood dependent contrast enhancement process is proposed to enhance the high frequency components of the illuminance, which compensates for the contrast degradation of the mid-tone frequency components caused by dynamic range compression. The intensity image obtained by integrating the enhanced illuminance and the extracted reflectance is then converted to a RGB color image through linear color restoration utilizing the color components of the original image. The second technique, named AINDANE, is a two step approach comprised of adaptive luminance enhancement and adaptive contrast enhancement. An image dependent nonlinear transfer function is designed for dynamic range compression and a multiscale image dependent neighborhood approach is developed for contrast enhancement. Real time processing of video streams is realized with the I-R model based technique due to its high speed processing capability while AINDANE produces higher quality enhanced images due to its multi-scale contrast enhancement property. Both the algorithms exhibit balanced luminance, contrast enhancement, higher robustness, and better color consistency when compared with conventional techniques.
In the transform domain approach, wavelet transform based image denoising and contrast enhancement algorithms are developed. The denoising is treated as a maximum a posteriori (MAP) estimator problem; a Bivariate probability density function model is introduced to explore the interlevel dependency among the wavelet coefficients. In addition, an approximate solution to the MAP estimation problem is proposed to avoid the use of complex iterative computations to find a numerical solution. This relatively low complexity image denoising algorithm implemented with dual-tree complex wavelet transform (DT-CWT) produces high quality denoised images
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