2,608 research outputs found
Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach
The main aim of this work is the development of a vision-based road detection
system fast enough to cope with the difficult real-time constraints imposed by
moving vehicle applications. The hardware platform, a special-purpose massively
parallel system, has been chosen to minimize system production and operational
costs. This paper presents a novel approach to expectation-driven low-level
image segmentation, which can be mapped naturally onto mesh-connected massively
parallel SIMD architectures capable of handling hierarchical data structures.
The input image is assumed to contain a distorted version of a given template;
a multiresolution stretching process is used to reshape the original template
in accordance with the acquired image content, minimizing a potential function.
The distorted template is the process output.Comment: See http://www.jair.org/ for any accompanying file
Model-based estimation of off-highway road geometry using single-axis LADAR and inertial sensing
This paper applies some previously studied extended
Kalman filter techniques for planar road geometry estimation
to the domain of autonomous navigation of off-highway
vehicles. In this work, a clothoid model of the road geometry is
constructed and estimated recursively based on road features
extracted from single-axis LADAR range measurements. We
present a method for feature extraction of the road centerline
in the image plane, and describe its application to recursive
estimation of the road geometry. We analyze the performance of
our method against simulated motion of varied road geometries
and against closed-loop detection, tracking and following of
desert roads. Our method accomodates full 6 DOF motion of
the vehicle as it navigates, constructs consistent estimates of the
road geometry with respect to a fixed global reference frame,
and requires an estimate of the sensor pose for each range
measurement
Improving construction materials management practices in construction sites
Construction Materials Management is a vital function for improving productivity in construction projects. Poor materials management can often affect the overall construction time, quality and budget. Currently, the construction material management practice in Somalia is believed to be poorly performed. Lack of standardized construction materials management system is one of the key issues facing by the building industry in Mogadishu-Somalia. The aim of this study was to investigate the current practices of material management at construction sites in Mogadishu-Somalia. A questionnaire survey study design was used to explore construction materials management practices. Fifty questionnaires were distributed to project managers, project engineers, site engineers, engineer, and foreman, and they were received and analysed. The following data analysis techniques were used: descriptive statistics were conducted to report sample characteristics, reliability and validity analyses were performed to confirm robustness of the instrument, graphical presentation such as bar charts were developed, and finally Average Mean Index Scale were constructed. The study results reveals that, 46.7% of respondent’s organization obtain materials for sites without site requisition by site engineer provisions, while 28.9% of respondent’s organization procure materials for sites with site requisition by project manager provisions and 13.3% of respondent’s organization procure materials for site by engineer. The results indicated that currently there is no standardized and computerized construction materials management system applied in Somalia. The researcher concluded that all contracting companies are interested in using some techniques of managing construction materials such as creating and updating database for materials categories from local and international suppliers. Finally, researcher recommends to use computerized construction materials management systems to reduce effort and time, and to achieve more accurate results
GANav: Group-wise Attention Network for Classifying Navigable Regions in Unstructured Outdoor Environments
We present a new learning-based method for identifying safe and navigable
regions in off-road terrains and unstructured environments from RGB images. Our
approach consists of classifying groups of terrain classes based on their
navigability levels using coarse-grained semantic segmentation. We propose a
bottleneck transformer-based deep neural network architecture that uses a novel
group-wise attention mechanism to distinguish between navigability levels of
different terrains.Our group-wise attention heads enable the network to
explicitly focus on the different groups and improve the accuracy. In addition,
we propose a dynamic weighted cross entropy loss function to handle the
long-tailed nature of the dataset. We show through extensive evaluations on the
RUGD and RELLIS-3D datasets that our learning algorithm improves the accuracy
of visual perception in off-road terrains for navigation. We compare our
approach with prior work on these datasets and achieve an improvement over the
state-of-the-art mIoU by 6.74-39.1% on RUGD and 3.82-10.64% on RELLIS-3D
Challenges and solutions for autonomous ground robot scene understanding and navigation in unstructured outdoor environments: A review
The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges
MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving
Autonomous driving requires operation in different behavioral modes ranging
from lane following and intersection crossing to turning and stopping. However,
most existing deep learning approaches to autonomous driving do not consider
the behavioral mode in the training strategy. This paper describes a technique
for learning multiple distinct behavioral modes in a single deep neural network
through the use of multi-modal multi-task learning. We study the effectiveness
of this approach, denoted MultiNet, using self-driving model cars for driving
in unstructured environments such as sidewalks and unpaved roads. Using labeled
data from over one hundred hours of driving our fleet of 1/10th scale model
cars, we trained different neural networks to predict the steering angle and
driving speed of the vehicle in different behavioral modes. We show that in
each case, MultiNet networks outperform networks trained on individual modes
while using a fraction of the total number of parameters.Comment: Published in IEEE WACV 201
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