1,247 research outputs found
LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance System
Collision avoidance is a critical task in many applications, such as ADAS
(advanced driver-assistance systems), industrial automation and robotics. In an
industrial automation setting, certain areas should be off limits to an
automated vehicle for protection of people and high-valued assets. These areas
can be quarantined by mapping (e.g., GPS) or via beacons that delineate a
no-entry area. We propose a delineation method where the industrial vehicle
utilizes a LiDAR {(Light Detection and Ranging)} and a single color camera to
detect passive beacons and model-predictive control to stop the vehicle from
entering a restricted space. The beacons are standard orange traffic cones with
a highly reflective vertical pole attached. The LiDAR can readily detect these
beacons, but suffers from false positives due to other reflective surfaces such
as worker safety vests. Herein, we put forth a method for reducing false
positive detection from the LiDAR by projecting the beacons in the camera
imagery via a deep learning method and validating the detection using a neural
network-learned projection from the camera to the LiDAR space. Experimental
data collected at Mississippi State University's Center for Advanced Vehicular
Systems (CAVS) shows the effectiveness of the proposed system in keeping the
true detection while mitigating false positives.Comment: 34 page
CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
3D LiDARs and 2D cameras are increasingly being used alongside each other in
sensor rigs for perception tasks. Before these sensors can be used to gather
meaningful data, however, their extrinsics (and intrinsics) need to be
accurately calibrated, as the performance of the sensor rig is extremely
sensitive to these calibration parameters. A vast majority of existing
calibration techniques require significant amounts of data and/or calibration
targets and human effort, severely impacting their applicability in large-scale
production systems. We address this gap with CalibNet: a self-supervised deep
network capable of automatically estimating the 6-DoF rigid body transformation
between a 3D LiDAR and a 2D camera in real-time. CalibNet alleviates the need
for calibration targets, thereby resulting in significant savings in
calibration efforts. During training, the network only takes as input a LiDAR
point cloud, the corresponding monocular image, and the camera calibration
matrix K. At train time, we do not impose direct supervision (i.e., we do not
directly regress to the calibration parameters, for example). Instead, we train
the network to predict calibration parameters that maximize the geometric and
photometric consistency of the input images and point clouds. CalibNet learns
to iteratively solve the underlying geometric problem and accurately predicts
extrinsic calibration parameters for a wide range of mis-calibrations, without
requiring retraining or domain adaptation. The project page is hosted at
https://epiception.github.io/CalibNetComment: Appeared in the proccedings of the IEEE International Conference on
Intelligent Robots and Systems (IROS) 201
Multimodal perception for autonomous driving
Mención Internacional en el tÃtulo de doctorAutonomous driving is set to play an important role among intelligent
transportation systems in the coming decades. The advantages
of its large-scale implementation –reduced accidents, shorter commuting
times, or higher fuel efficiency– have made its development a priority
for academia and industry. However, there is still a long way to
go to achieve full self-driving vehicles, capable of dealing with any
scenario without human intervention. To this end, advances in control,
navigation and, especially, environment perception technologies
are yet required. In particular, the detection of other road users that
may interfere with the vehicle’s trajectory is a key element, since it
allows to model the current traffic situation and, thus, to make decisions
accordingly.
The objective of this thesis is to provide solutions to some of
the main challenges of on-board perception systems, such as extrinsic
calibration of sensors, object detection, and deployment on
real platforms. First, a calibration method for obtaining the relative
transformation between pairs of sensors is introduced, eliminating
the complex manual adjustment of these parameters. The algorithm
makes use of an original calibration pattern and supports LiDARs,
and monocular and stereo cameras. Second, different deep learning
models for 3D object detection using LiDAR data in its bird’s eye
view projection are presented. Through a novel encoding, the use
of architectures tailored to image detection is proposed to process
the 3D information of point clouds in real time. Furthermore, the
effectiveness of using this projection together with image features is
analyzed. Finally, a method to mitigate the accuracy drop of LiDARbased
detection networks when deployed in ad-hoc configurations is
introduced. For this purpose, the simulation of virtual signals mimicking
the specifications of the desired real device is used to generate
new annotated datasets that can be used to train the models.
The performance of the proposed methods is evaluated against
other existing alternatives using reference benchmarks in the field of
computer vision (KITTI and nuScenes) and through experiments in
open traffic with an automated vehicle. The results obtained demonstrate
the relevance of the presented work and its suitability for commercial
use.La conducción autónoma está llamada a jugar un papel importante en
los sistemas inteligentes de transporte de las próximas décadas. Las
ventajas de su implementación a larga escala –disminución de accidentes,
reducción del tiempo de trayecto, u optimización del consumo–
han convertido su desarrollo en una prioridad para la academia y
la industria. Sin embargo, todavÃa hay un largo camino por delante
hasta alcanzar una automatización total, capaz de enfrentarse a cualquier
escenario sin intervención humana. Para ello, aún se requieren
avances en las tecnologÃas de control, navegación y, especialmente,
percepción del entorno. Concretamente, la detección de otros usuarios
de la carretera que puedan interferir en la trayectoria del vehÃculo
es una pieza fundamental para conseguirlo, puesto que permite modelar
el estado actual del tráfico y tomar decisiones en consecuencia.
El objetivo de esta tesis es aportar soluciones a algunos de los
principales retos de los sistemas de percepción embarcados, como
la calibración extrÃnseca de los sensores, la detección de objetos, y su
despliegue en plataformas reales. En primer lugar, se introduce un
método para la obtención de la transformación relativa entre pares
de sensores, eliminando el complejo ajuste manual de estos parámetros.
El algoritmo hace uso de un patrón de calibración propio y da
soporte a cámaras monoculares, estéreo, y LiDAR. En segundo lugar,
se presentan diferentes modelos de aprendizaje profundo para la detección
de objectos en 3D utilizando datos de escáneres LiDAR en su
proyección en vista de pájaro. A través de una nueva codificación, se
propone la utilización de arquitecturas de detección en imagen para
procesar en tiempo real la información tridimensional de las nubes
de puntos. Además, se analiza la efectividad del uso de esta proyección
junto con caracterÃsticas procedentes de imágenes. Por último,
se introduce un método para mitigar la pérdida de precisión de las
redes de detección basadas en LiDAR cuando son desplegadas en
configuraciones ad-hoc. Para ello, se plantea la simulación de señales
virtuales con las caracterÃsticas del modelo real que se quiere utilizar,
generando asà nuevos conjuntos anotados para entrenar los modelos.
El rendimiento de los métodos propuestos es evaluado frente a
otras alternativas existentes haciendo uso de bases de datos de referencia
en el campo de la visión por computador (KITTI y nuScenes),
y mediante experimentos en tráfico abierto empleando un vehÃculo
automatizado. Los resultados obtenidos demuestran la relevancia de
los trabajos presentados y su viabilidad para un uso comercial.Programa de Doctorado en IngenierÃa Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Jesús GarcÃa Herrero.- Secretario: Ignacio Parra Alonso.- Vocal: Gustavo Adolfo Peláez Coronad
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