2,062 research outputs found
Tracking and Fusion Methods for Extended Targets Parameterized by Center, Orientation, and Semi-axes
The improvements in sensor technology, e.g., the development of automotive Radio Detection and
Ranging (RADAR) or Light Detection and Ranging (LIDAR), which are able to provide a higher
detail of the sensor’s environment, have introduced new opportunities but also new challenges to
target tracking. In classic target tracking, targets are assumed as points. However, this assumption
is no longer valid if targets occupy more than one sensor resolution cell, creating the need for
extended targets, modeling the shape in addition to the kinematic parameters. Different shape
models are possible and this thesis focuses on an elliptical shape, parameterized with center,
orientation, and semi-axes lengths. This parameterization can be used to model rectangles as well.
Furthermore, this thesis is concerned with multi-sensor fusion for extended targets, which can be
used to improve the target tracking by providing information gathered from different sensors or
perspectives. We also consider estimation of extended targets, i.e., to account for uncertainties, the
target is modeled by a probability density, so we need to find a so-called point estimate.
Extended target tracking provides a variety of challenges due to the spatial extent, which need
to be handled, even for basic shapes like ellipses and rectangles. Among these challenges are the
choice of the target model, e.g., how the measurements are distributed across the shape. Additional
challenges arise for sensor fusion, as it is unclear how to best consider the geometric properties
when combining two extended targets. Finally, the extent needs to be involved in the estimation.
Traditional methods often use simple uniform distributions across the shape, which do not properly
portray reality, while more complex methods require the use of optimization techniques or large
amounts of data. In addition, for traditional estimation, metrics such as the Euclidean distance
between state vectors are used. However, they might no longer be valid because they do not
consider the geometric properties of the targets’ shapes, e.g., rotating an ellipse by 180 degree
results in the same ellipse, but the Euclidean distance between them is not 0. In multi-sensor fusion,
the same holds, i.e., simply combining the corresponding elements of the state vectors can lead to
counter-intuitive fusion results.
In this work, we compare different elliptic trackers and discuss more complex measurement
distributions across the shape’s surface or contour. Furthermore, we discuss the problems which
can occur when fusing extended target estimates from different sensors and how to handle them
by providing a transformation into a special density. We then proceed to discuss how a different
metric, namely the Gaussian Wasserstein (GW) distance, can be used to improve target estimation.
We define an estimator and propose an approximation based on an extension of the square root
distance. It can be applied on the posterior densities of the aforementioned trackers to incorporate
the unique properties of ellipses in the estimation process. We also discuss how this can be applied
to rectangular targets as well. Finally, we evaluate and discuss our approaches. We show the
benefits of more complex target models in simulations and on real data and we demonstrate our
estimation and fusion approaches compared to classic methods on simulated data.2022-01-2
Automotive Target Models for Point Cloud Sensors
One of the major challenges to enable automated driving is the perception of other road users in
the host vehicle’s vicinity. Various automotive sensors that provide detailed information about
other traffic participants have been developed to handle this challenge. Of particular interest for
this work are Light Detection and Ranging (LIDAR) and Radio Detection and Ranging (RADAR)
sensors, which generate multiple, spatially distributed, noise corrupted point measurements on
other traffic participants. Based on these point measurements, the traffic participant’s kinematic
and shape parameters have to be estimated.
The choice of a suitable extent model is paramount to accurately track a target’s position, orientation
and other parameters. How well a model performs typically depends on the type of target that
has to be tracked, e.g. pedestrians, bikes or cars, as well as the sensor’s setup and measurement
principle itself. This work considers the creation of extended object models and corresponding
inference strategies for tracking automotive vehicles based on accumulated point cloud data.
We gain insights into the extended object model’s requirements by analysing automotive LIDAR
and RADAR sensor data. This analysis aids in the identification of relevant features from the
measurement’s spatial distribution and their incorporation into an accurate target model. The
analysis lays the foundation for our main contributions.
We developed a constrained Spline-based geometric representation and a corresponding inference
strategy for the contour of cars in LIDAR data.
We further developed a heuristic to account for the integration of the measurement distribution on
cars, generated by LIDAR sensors mounted on the roof of the recording vessel.
Last, we developed an extended target model for cars based on automotive RADAR sensors. The
model provides an interpretation of a learned Gaussian Mixture Model (GMM) as scatter sources
and uses the Probabilistic Multi-Hypothesis Tracker (PMHT) to formulate a closed form Maximum
a Posteriori (MAP) update.
All developed approaches are evaluated on real world data sets.2022-02-0
A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics
We survey the current state of millimeterwave (mmWave) radar applications in
robotics with a focus on unique capabilities, and discuss future opportunities
based on the state of the art. Frequency Modulated Continuous Wave (FMCW)
mmWave radars operating in the 76--81GHz range are an appealing alternative to
lidars, cameras and other sensors operating in the near visual spectrum. Radar
has been made more widely available in new packaging classes, more convenient
for robotics and its longer wavelengths have the ability to bypass visual
clutter such as fog, dust, and smoke. We begin by covering radar principles as
they relate to robotics. We then review the relevant new research across a
broad spectrum of robotics applications beginning with motion estimation,
localization, and mapping. We then cover object detection and classification,
and then close with an analysis of current datasets and calibration techniques
that provide entry points into radar research.Comment: 19 Pages, 11 Figures, 2 Tables, TRO Submission pendin
Anomaly Detection in Autonomous Driving: A Survey
Nowadays, there are outstanding strides towards a future with autonomous
vehicles on our roads. While the perception of autonomous vehicles performs
well under closed-set conditions, they still struggle to handle the unexpected.
This survey provides an extensive overview of anomaly detection techniques
based on camera, lidar, radar, multimodal and abstract object level data. We
provide a systematization including detection approach, corner case level,
ability for an online application, and further attributes. We outline the
state-of-the-art and point out current research gaps.Comment: Daniel Bogdoll and Maximilian Nitsche contributed equally. Accepted
for publication at CVPR 2022 WAD worksho
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems
Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300
GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including
security sensing, industrial packaging, medical imaging, and non-destructive
testing. Traditional methods for perception and imaging are challenged by novel
data-driven algorithms that offer improved resolution, localization, and
detection rates. Over the past decade, deep learning technology has garnered
substantial popularity, particularly in perception and computer vision
applications. Whereas conventional signal processing techniques are more easily
generalized to various applications, hybrid approaches where signal processing
and learning-based algorithms are interleaved pose a promising compromise
between performance and generalizability. Furthermore, such hybrid algorithms
improve model training by leveraging the known characteristics of radio
frequency (RF) waveforms, thus yielding more efficiently trained deep learning
algorithms and offering higher performance than conventional methods. This
dissertation introduces novel hybrid-learning algorithms for improved mmWave
imaging systems applicable to a host of problems in perception and sensing.
Various problem spaces are explored, including static and dynamic gesture
classification; precise hand localization for human computer interaction;
high-resolution near-field mmWave imaging using forward synthetic aperture
radar (SAR); SAR under irregular scanning geometries; mmWave image
super-resolution using deep neural network (DNN) and Vision Transformer (ViT)
architectures; and data-level multiband radar fusion using a novel
hybrid-learning architecture. Furthermore, we introduce several novel
approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen
Aprendizagem automática aplicada à deteção de pessoas baseada em radar
The present dissertation describes the development and implementation of a
radar-based system with the purpose of being able to detect people amidst
other objects that are moving in an indoor scenario. The detection methods
implemented exploit radar data that is processed by a system that includes the
data acquisition, the pre-processing of the data, the feature extraction, and the
application of these data to machine learning models specifically designed to
attain the objective of target classification.
Beyond the basic theoretical research necessary for its sucessful development,
the work contamplates an important component of software development
and experimental tests. Among others, the following topics were covered
in this dissertation: the study of radar working principles and hardware; radar
signal processing; techniques of clutter removal, feature exctraction, and data
clustering applied to radar signals; implementation and hyperparameter tuning
of machine learning classification systems; study of multi-target detection and
tracking methods.
The people detection application was tested in different indoor scenarios that
include a static radar and a radar dynamically deployed by a mobile robot. This
application can be executed in real time and perform multiple target detection
and classification using basic clustering and tracking algorithms. A study of
the effects of the detection of multiple targets in the performance of the application,
as well as an assessment of the efficiency of the different classification
methods is presented.
The envisaged applications of the proposed detection system include intrusion
detection in indoor environments and acquisition of anonymized data for
people tracking and counting in public spaces such as hospitals and schools.A presente dissertação descreve o desenvolvimento e implementação de um
sistema baseado em radar que tem como objetivo detetar e distinguir pessoas
de outros objetos que se movem num ambiente interior. Os métodos de deteção
e distinção exploram os dados de radar que são processados por um
sistema que abrange a aquisição e pré-processamento dos dados, a extração
de caracterÃsticas, e a aplicação desses dados a modelos de aprendizagem
automática especificamente desenhados para atingir o objetivo de classificação
de alvos.
Além do estudo da teoria básica de radar para o desenvolvimento bem sucedido
desta dissertação, este trabalho contempla uma componente importante
de desenvolvimento de software e testes experimentais. Entre outros,
os seguintes tópicos foram abordados nesta dissertação: o estudo dos
princÃpios básicos do funcionamento do radar e do seu equipamento; processamento
de sinal do radar; técnicas de remoção de ruÃdo, extração de
caracterÃsticas, e segmentação de dados aplicada ao sinal de radar; implementação
e calibração de hiper-parâmetros dos modelos de aprendizagem
automática para sistemas de classificação; estudo de métodos de deteção e
seguimento de múltiplos alvos.
A aplicação para deteção de pessoas foi testada em diferentes cenários interiores
que incluem o radar estático ou transportado por um robot móvel.
Esta aplicação pode ser executada em tempo real e realizar deteção e classificação
de múltiplos alvos usando algoritmos básicos de segmentação e
seguimento. O estudo do impacto da deteção de múltiplos alvos no funcionamento
da aplicação é apresentado, bem como a avaliação da eficiência dos
diferentes métodos de classificação usados.
As possÃveis aplicações do sistema de deteção proposto incluem a deteção
de intrusão em ambientes interiores e aquisição de dados anónimos para
seguimento e contagem de pessoas em espaços públicos tais como hospitais
ou escolas.Mestrado em Engenharia de Computadores e Telemátic
Three-Dimensional Extended Object Tracking and Shape Learning Using Gaussian Processes
In this study, we investigate the problem of tracking objects with unknown
shapes using three-dimensional (3D) point cloud data. We propose a Gaussian
process-based model to jointly estimate object kinematics, including position,
orientation and velocities, together with the shape of the object for online
and offline applications. We describe the unknown shape by a radial function in
3D, and induce a correlation structure via a Gaussian process. Furthermore, we
propose an efficient algorithm to reduce the computational complexity of
working with 3D data. This is accomplished by casting the tracking problem into
projection planes which are attached to the object's local frame. The resulting
algorithms can process 3D point cloud data and accomplish tracking of a dynamic
object. Furthermore, they provide analytical expressions for the representation
of the object shape in 3D, together with confidence intervals. The confidence
intervals, which quantify the uncertainty in the shape estimate, can later be
used for solving the gating and association problems inherent in object
tracking. The performance of the methods is demonstrated both on simulated and
real data. The results are compared with an existing random matrix model, which
is commonly used for extended object tracking in the literature
Long-Term Localization for Self-Driving Cars
Long-term localization is hard due to changing conditions, while relative localization within time sequences is much easier. To achieve long-term localization in a sequential setting, such as, for self-driving cars, relative localization should be used to the fullest extent, whenever possible.This thesis presents solutions and insights both for long-term sequential visual localization, and localization using global navigational satellite systems (GNSS), that push us closer to the goal of accurate and reliable localization for self-driving cars. It addresses the question: How to achieve accurate and robust, yet cost-effective long-term localization for self-driving cars?Starting in this question, the thesis explores how existing sensor suites for advanced driver-assistance systems (ADAS) can be used most efficiently, and how landmarks in maps can be recognized and used for localization even after severe changes in appearance. The findings show that:* State-of-the-art ADAS sensors are insufficient to meet the requirements for localization of a self-driving car in less than ideal conditions.GNSS and visual localization are identified as areas to improve.\ua0* Highly accurate relative localization with no convergence delay is possible by using time relative GNSS observations with a single band receiver, and no base stations.\ua0* Sequential semantic localization is identified as a promising focus point for further research based on a benchmark study comparing state-of-the-art visual localization methods in challenging autonomous driving scenarios including day-to-night and seasonal changes.\ua0* A novel sequential semantic localization algorithm improves accuracy while significantly reducing map size compared to traditional methods based on matching of local image features.\ua0* Improvements for semantic segmentation in challenging conditions can be made efficiently by automatically generating pixel correspondences between images from a multitude of conditions and enforcing a consistency constraint during training.\ua0* A segmentation algorithm with automatically defined and more fine-grained classes improves localization performance.\ua0* The performance advantage seen in single image localization for modern local image features, when compared to traditional ones, is all but erased when considering sequential data with odometry, thus, encouraging to focus future research more on sequential localization, rather than pure single image localization
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