350 research outputs found
Under vehicle perception for high level safety measures using a catadioptric camera system
In recent years, under vehicle surveillance and the classification of the vehicles become an indispensable task that must be achieved for security measures in certain areas such as shopping centers, government buildings, army camps etc. The main challenge to achieve this task is to monitor the under
frames of the means of transportations. In this paper, we present a novel solution to achieve this aim. Our solution consists of three main parts: monitoring, detection and classification. In the first part we design a new catadioptric camera system in which the perspective camera points downwards to the catadioptric mirror mounted to the body of a mobile robot. Thanks to the
catadioptric mirror the scenes against the camera optical axis direction can be viewed. In the second part we use speeded up robust features (SURF) in an object recognition algorithm. Fast appearance based mapping algorithm (FAB-MAP) is exploited for the classification of the means of transportations in the third
part. Proposed technique is implemented in a laboratory environment
Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors
Visual localization is an attractive problem that estimates the camera
localization from database images based on the query image. It is a crucial
task for various applications, such as autonomous vehicles, assistive
navigation and augmented reality. The challenging issues of the task lie in
various appearance variations between query and database images, including
illumination variations, dynamic object variations and viewpoint variations. In
order to tackle those challenges, Panoramic Annular Localizer into which
panoramic annular lens and robust deep image descriptors are incorporated is
proposed in this paper. The panoramic annular images captured by the single
camera are processed and fed into the NetVLAD network to form the active deep
descriptor, and sequential matching is utilized to generate the localization
result. The experiments carried on the public datasets and in the field
illustrate the validation of the proposed system.Comment: Accepted by ITSC 201
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
A Fisher-Rao metric for paracatadioptric images of lines
In a central paracatadioptric imaging system a perspective camera takes an image of a scene reflected in a paraboloidal mirror. A 360° field of view is obtained, but
the image is severely distorted. In particular, straight lines in the scene project to circles in the image. These distortions make it diffcult to detect projected lines using standard image processing algorithms. The distortions are removed using a Fisher-Rao metric which is defined on the space of projected lines in the paracatadioptric image. The space of projected lines is divided into subsets such that on each subset the Fisher-Rao metric is closely approximated by the Euclidean metric. Each subset is sampled at the vertices of a square grid and values are assigned to the sampled points using an adaptation of the trace transform. The result is a set of digital images to which standard image processing algorithms can be applied.
The effectiveness of this approach to line detection is illustrated using two algorithms, both of which are based on the Sobel edge operator. The task of line detection is reduced to the task of finding isolated peaks in a Sobel image. An experimental comparison is made between these two algorithms and third algorithm taken from the literature and
based on the Hough transform
A 3D Omnidirectional Sensor For Mobile Robot Applications
International audienc
Fast computational processing for mobile robots' self-localization
This paper intends to present a different approach
to solve the Self-Localization problem regarding a RoboCup’s
Middle Size League game, developed by MINHO team
researchers. The method uses white field markings as key points,
to compute the position with least error, creating an error-based
graphic where the minimum corresponds to the real position,
that are computed by comparing the key (line) points with a
precomputed set of values for each position. This approach
allows a very fast local and global localization calculation,
allowing the global localization to be used more often, while
driving the estimate to its real value. Differently from the
majority of other teams in this league, it was important to come
up with a new and improved method to solve the traditional slow
Self-Localization problem.This work was developed at the Laboratório de Automação e Robótica by MINHO team´s researching and developing team, at University of Minho, under the supervision of Professor A. Fernando Ribeiro and A. Gil Lopes. The knowledge exchanging between the RoboCup’s MSL teams and community contributed greatly for the development of this work. This work has been supported by COMPETE: POCI-01- 0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio
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