241 research outputs found
Mapping and Localization in Urban Environments Using Cameras
In this work we present a system to fully automatically create a highly accurate visual feature map from image data aquired from within a moving vehicle. Moreover, a system for high precision self localization is presented. Furthermore, we present a method to automatically learn a visual descriptor. The map relative self localization is centimeter accurate and allows autonomous driving
Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey
The Internet of Underwater Things (IoUT) is an emerging communication
ecosystem developed for connecting underwater objects in maritime and
underwater environments. The IoUT technology is intricately linked with
intelligent boats and ships, smart shores and oceans, automatic marine
transportations, positioning and navigation, underwater exploration, disaster
prediction and prevention, as well as with intelligent monitoring and security.
The IoUT has an influence at various scales ranging from a small scientific
observatory, to a midsized harbor, and to covering global oceanic trade. The
network architecture of IoUT is intrinsically heterogeneous and should be
sufficiently resilient to operate in harsh environments. This creates major
challenges in terms of underwater communications, whilst relying on limited
energy resources. Additionally, the volume, velocity, and variety of data
produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise
to the concept of Big Marine Data (BMD), which has its own processing
challenges. Hence, conventional data processing techniques will falter, and
bespoke Machine Learning (ML) solutions have to be employed for automatically
learning the specific BMD behavior and features facilitating knowledge
extraction and decision support. The motivation of this paper is to
comprehensively survey the IoUT, BMD, and their synthesis. It also aims for
exploring the nexus of BMD with ML. We set out from underwater data collection
and then discuss the family of IoUT data communication techniques with an
emphasis on the state-of-the-art research challenges. We then review the suite
of ML solutions suitable for BMD handling and analytics. We treat the subject
deductively from an educational perspective, critically appraising the material
surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys &
Tutorials, peer-reviewed academic journa
Development of a probabilistic perception system for camera-lidar sensor fusion
La estimación de profundidad usando diferentes sensores es uno de los desafÃos clave para dotar a las máquinas autónomas de sólidas capacidades de percepción robótica. Ha habido un avance sobresaliente en el desarrollo de técnicas de estimación de profundidad unimodales basadas en cámaras monoculares, debido a su alta resolución o sensores LiDAR, debido a los datos geométricos precisos que proporcionan. Sin embargo, cada uno de ellos presenta inconvenientes inherentes, como la alta sensibilidad a los cambios en las condiciones de iluminación en el caso delas cámaras y la resolución limitada de los sensores LiDAR. La fusión de sensores se puede utilizar para combinar los méritos y compensar las desventajas de estos dos tipos de sensores. Sin embargo, los métodos de fusión actuales funcionan a un alto nivel. Procesan los flujos de datos de los sensores de forma independiente y combinan las estimaciones de alto nivel obtenidas para cada sensor. En este proyecto, abordamos el problema en un nivel bajo, fusionando los flujos de sensores sin procesar, obteniendo asà estimaciones de profundidad que son densas y precisas, y pueden usarse como una fuente de datos multimodal unificada para problemas de estimación de nivel superior. Este trabajo propone un modelo de campo aleatorio condicional (CRF) con múltiples potenciales de geometrÃa y apariencia que representa a la perfección el problema de estimar mapas de profundidad densos a partir de datos de cámara y LiDAR. El modelo se puede optimizar de manera eficiente utilizando el algoritmo Conjúgate Gradient Squared (CGS). El método propuesto se evalúa y compara utilizando el conjunto de datos proporcionado por KITTI Datset. Adicionalmente, se evalúa cualitativamente el modelo, usando datos adquiridos por el autor de esté trabajoMulti-modal depth estimation is one of the key challenges for endowing autonomous
machines with robust robotic perception capabilities. There has been an outstanding
advance in the development of uni-modal depth estimation techniques based
on either monocular cameras, because of their rich resolution or LiDAR sensors due
to the precise geometric data they provide. However, each of them suffers from some
inherent drawbacks like high sensitivity to changes in illumination conditions in
the case of cameras and limited resolution for the LiDARs. Sensor fusion can be
used to combine the merits and compensate the downsides of these two kinds of
sensors. Nevertheless, current fusion methods work at a high level. They processes
sensor data streams independently and combine the high level estimates obtained
for each sensor. In this thesis, I tackle the problem at a low level, fusing the raw
sensor streams, thus obtaining depth estimates which are both dense and precise,
and can be used as a unified multi-modal data source for higher level estimation
problems.
This work proposes a Conditional Random Field (CRF) model with multiple geometry
and appearance potentials that seamlessly represents the problem of estimating
dense depth maps from camera and LiDAR data. The model can be optimized
efficiently using the Conjugate Gradient Squared (CGS) algorithm. The proposed
method was evaluated and compared with the state-of-the-art using the commonly
used KITTI benchmark dataset. In addition, the model is qualitatively evaluated using
data acquired by the author of this work.MaestrÃaMagÃster en IngenierÃa de Desarrollo de Producto
Development of a probabilistic perception system for camera-lidar sensor fusion
La estimación de profundidad usando diferentes sensores es uno de los desafÃos clave para dotar a las máquinas autónomas de sólidas capacidades de percepción robótica. Ha habido un avance sobresaliente en el desarrollo de técnicas de estimación de profundidad unimodales basadas en cámaras monoculares, debido a su alta resolución o sensores LiDAR, debido a los datos geométricos precisos que proporcionan. Sin embargo, cada uno de ellos presenta inconvenientes inherentes, como la alta sensibilidad a los cambios en las condiciones de iluminación en el caso delas cámaras y la resolución limitada de los sensores LiDAR. La fusión de sensores se puede utilizar para combinar los méritos y compensar las desventajas de estos dos tipos de sensores. Sin embargo, los métodos de fusión actuales funcionan a un alto nivel. Procesan los flujos de datos de los sensores de forma independiente y combinan las estimaciones de alto nivel obtenidas para cada sensor. En este proyecto, abordamos el problema en un nivel bajo, fusionando los flujos de sensores sin procesar, obteniendo asà estimaciones de profundidad que son densas y precisas, y pueden usarse como una fuente de datos multimodal unificada para problemas de estimación de nivel superior. Este trabajo propone un modelo de campo aleatorio condicional (CRF) con múltiples potenciales de geometrÃa y apariencia que representa a la perfección el problema de estimar mapas de profundidad densos a partir de datos de cámara y LiDAR. El modelo se puede optimizar de manera eficiente utilizando el algoritmo Conjúgate Gradient Squared (CGS). El método propuesto se evalúa y compara utilizando el conjunto de datos proporcionado por KITTI Datset. Adicionalmente, se evalúa cualitativamente el modelo, usando datos adquiridos por el autor de esté trabajoMulti-modal depth estimation is one of the key challenges for endowing autonomous
machines with robust robotic perception capabilities. There has been an outstanding
advance in the development of uni-modal depth estimation techniques based
on either monocular cameras, because of their rich resolution or LiDAR sensors due
to the precise geometric data they provide. However, each of them suffers from some
inherent drawbacks like high sensitivity to changes in illumination conditions in
the case of cameras and limited resolution for the LiDARs. Sensor fusion can be
used to combine the merits and compensate the downsides of these two kinds of
sensors. Nevertheless, current fusion methods work at a high level. They processes
sensor data streams independently and combine the high level estimates obtained
for each sensor. In this thesis, I tackle the problem at a low level, fusing the raw
sensor streams, thus obtaining depth estimates which are both dense and precise,
and can be used as a unified multi-modal data source for higher level estimation
problems.
This work proposes a Conditional Random Field (CRF) model with multiple geometry
and appearance potentials that seamlessly represents the problem of estimating
dense depth maps from camera and LiDAR data. The model can be optimized
efficiently using the Conjugate Gradient Squared (CGS) algorithm. The proposed
method was evaluated and compared with the state-of-the-art using the commonly
used KITTI benchmark dataset. In addition, the model is qualitatively evaluated using
data acquired by the author of this work.MaestrÃaMagÃster en IngenierÃa de Desarrollo de Producto
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Real-time spatial modeling to detect and track resources on construction sites
For more than 10 years the U.S. construction industry has experienced over 1,000
fatalities annually. Many fatalities may have been prevented had the individuals and
equipment involved been more aware of and alert to the physical state of the environment
around them. Awareness may be improved by automatic 3D (three-dimensional) sensing
and modeling of the job site environment in real-time. Existing 3D modeling approaches
based on range scanning techniques are capable of modeling static objects only, and thus
cannot model in real-time dynamic objects in an environment comprised of moving
humans, equipment, and materials. Emerging prototype 3D video range cameras offer
another alternative by facilitating affordable, wide field of view, automated static and
dynamic object detection and tracking at frame rates better than 1Hz (real-time).
This dissertation presents an imperical work and methodology to rapidly create a
spatial model of construction sites and in particular to detect, model, and track the position, dimension, direction, and velocity of static and moving project resources in real-time, based on range data obtained from a three-dimensional video range camera in a
static or moving position. Existing construction site 3D modeling approaches based on
optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling
approaches (dense, sparse, etc.) that offered potential solutions for this research are
reviewed. The choice of an emerging sensing tool and preliminary experiments with this
prototype sensing technology are discussed. These findings led to the development of a
range data processing algorithm based on three-dimensional occupancy grids which is
demonstrated in detail. Testing and validation of the proposed algorithms have been
conducted to quantify the performance of sensor and algorithm through extensive
experimentation involving static and moving objects. Experiments in indoor laboratory
and outdoor construction environments have been conducted with construction resources
such as humans, equipment, materials, or structures to verify the accuracy of the
occupancy grid modeling approach. Results show that modeling objects and measuring
their position, dimension, direction, and speed had an accuracy level compatible to the
requirements of active safety features for construction. Results demonstrate that video
rate 3D data acquisition and analysis of construction environments can support effective
detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated
three-dimensional models for improved visualization, communications, and process
control has inherent value, broad application, and potential impact, e.g. as-built vs. as-planned comparison, condition assessment, maintenance, operations, and construction
activities control. In combination with effective management practices, this sensing
approach has the potential to assist equipment operators to avoid incidents that result in
reduce human injury, death, or collateral damage on construction sites.Civil, Architectural, and Environmental Engineerin
Multi-camera framework for object detection and distance estimation
Dissertação (mestrado)—Universidade de BrasÃlia, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2020.Os veÃculos autônomos podem reduzir o número de acidentes automobilÃsticos e o número de vÃtimas fatais. Segundo o Departamento de EstatÃstica da Alemanha, apenas em 2019, ocorreram mais de 2 milhões de acidentes de carro. Este trabalho propõe um sistema multi câmera para detecção de objetos e medição de distâncias por visão computacional que irá apoiar alguns testes de veÃculos autônomos e melhorar a segurança durante os testes. Três abordagens são realizadas e comparadas com a distância real, e apenas a melhor técnica foi incluÃda no framework proposto. Na maioria dos casos, esse erro está diretamente relacionado a fatores meteorológicos e sinais de comunicação fracos entre as câmeras e o hardware de controle. Os resultados obtidos mostram que os métodos de detecção de objetos garantem precisão com exatidão acima de 93 % em condições ideais e ambientes controlados. No entanto, a precisão é reduzida quando os obstáculos estão presentes na frente do objeto detectado. Técnicas adicionais também são propostas para otimizar o posicionamento das câmeras e o ângulo de inclinação.Autonomous Vehicles can reduce the number of car crashes and the number of fatal victims.
Following the German Statistical Department, just in 2019, there were over 2 million car
accidents, and more than 90 percent of crashes are caused by human errors (National Highway
Traffic Safety Administration, 2015). This work proposes a multi-camera system for object
detection and distance measurement using computer vision that it will support some
autonomous vehicle tests and improve safety during the tests. Three approaches are performed
and compared with the real distance, and just the best technique was included in the proposed
framework. In most cases, this error is directly related to meteorological factors and weak
communication signals between cameras and the control hardware. The results obtained show
that object detection methods guarantee precision with an accuracy above 93 % in ideal
conditions and controlled environments. However, accuracy is reduced when obstacles are
present in front of the detected object. Additional techniques are also proposed to optimize the
positioning of the cameras and the angle of inclination
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