2,169 research outputs found
Scan matching by cross-correlation and differential evolution
Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85
Active SLAM for autonomous underwater exploration
Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version
Novel point-to-point scan matching algorithm based on cross-correlation
The localization of mobile robots in outdoor and indoor environments is a complex issue. Many sophisticated approaches, based
on various types of sensory inputs and different computational concepts, are used to accomplish this task. However, many of the
most efficient methods for mobile robot localization suffer from high computational costs and/or the need for high resolution
sensory inputs. Scan cross-correlation is a traditional approach that can be, in special cases, used to match temporally aligned scans
of robot environment. This work proposes a set of novel modifications to the cross-correlation method that extend its capability
beyond these special cases to general scan matching and mitigate its computational costs so that it is usable in practical settings.
The properties and validity of the proposed approach are in this study illustrated on a number of computational experiments.Web of Scienceart. ID 646394
Radar-only ego-motion estimation in difficult settings via graph matching
Radar detects stable, long-range objects under variable weather and lighting
conditions, making it a reliable and versatile sensor well suited for
ego-motion estimation. In this work, we propose a radar-only odometry pipeline
that is highly robust to radar artifacts (e.g., speckle noise and false
positives) and requires only one input parameter. We demonstrate its ability to
adapt across diverse settings, from urban UK to off-road Iceland, achieving a
scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS
as ground truth (compared to visual odometry's 5.77 cm and 0.1032 deg). We
present algorithms for keypoint extraction and data association, framing the
latter as a graph matching optimization problem, and provide an in-depth system
analysis.Comment: 6 content pages, 1 page of references, 5 figures, 4 tables, 2019 IEEE
International Conference on Robotics and Automation (ICRA
3D Reconstruction & Assessment Framework based on affordable 2D Lidar
Lidar is extensively used in the industry and mass-market. Due to its
measurement accuracy and insensitivity to illumination compared to cameras, It
is applied onto a broad range of applications, like geodetic engineering, self
driving cars or virtual reality. But the 3D Lidar with multi-beam is very
expensive, and the massive measurements data can not be fully leveraged on some
constrained platforms. The purpose of this paper is to explore the possibility
of using cheap 2D Lidar off-the-shelf, to preform complex 3D Reconstruction,
moreover, the generated 3D map quality is evaluated by our proposed metrics at
the end. The 3D map is constructed in two ways, one way in which the scan is
performed at known positions with an external rotary axis at another plane. The
other way, in which the 2D Lidar for mapping and another 2D Lidar for
localization are placed on a trolley, the trolley is pushed on the ground
arbitrarily. The generated maps by different approaches are converted to
octomaps uniformly before the evaluation. The similarity and difference between
two maps will be evaluated by the proposed metrics thoroughly. The whole
mapping system is composed of several modular components. A 3D bracket was made
for assembling of the Lidar with a long range, the driver and the motor
together. A cover platform made for the IMU and 2D Lidar with a shorter range
but high accuracy. The software is stacked up in different ROS packages.Comment: 7 pages, 9 Postscript figures. Accepted by 2018 IEEE International
Conference on Advanced Intelligent Mechatronic
Evolutionary Optimization Techniques for 3D Simultaneous Localization and Mapping
Mención Internacional en el título de doctorMobile robots are growing up in applications to move through indoors and outdoors environments,
passing from teleoperated applications to autonomous applications like exploring
or navigating. For a robot to move through a particular location, it needs to gather information
about the scenario using sensors. These sensors allow the robot to observe, depending on the
sensor data type. Cameras mostly give information in two dimensions, with colors and pixels
representing an image. Range sensors give distances from the robot to obstacles. Depth
Cameras mix both technologies to expand their information to three-dimensional information.
Light Detection and Ranging (LiDAR) provides information about the distance to the sensor
but expands its range to planes and three dimensions alongside precision. So, mobile robots
use those sensors to scan the scenario while moving. If the robot already has a map, the sensors
measure, and the robot finds features that correspond to features on the map to localize
itself. Men have used Maps as a specialized form of representing the environment for more
than 5000 years, becoming a piece of important information in today’s daily basics. Maps are
used to navigate from one place to another, localize something inside some boundaries, or as
a form of documentation of essential features. So naturally, an intuitive way of making an
autonomous mobile robot is to implement geometrical information maps to represent the environment.
On the other hand, if the robot does not have a previous map, it should build it while
moving around. The robot computes the sensor information with the odometer sensor information
to achieve this task. However, sensors have their own flaws due to precision, calibration,
or accuracy. Furthermore, moving a robot has its physical constraints and faults that may occur
randomly, like wheel drifting or mechanical miscalibration that may make the odometers fail
in the measurement, causing misalignment during the map building. A novel technique was
presented in the mid-90s to solve this problem and overpass the uncertainty of sensors while
the robot is building the map, the Simultaneous Localization and Mapping algorithm (SLAM).
Its goal is to build a map while the robot’s position is corrected based on the information of
two or more consecutive scans matched together or find the rigid registration vector between
them. This algorithm has been broadly studied and developed for almost 25 years. Nonetheless,
it is highly relevant in innovations, modifications, and adaptations due to the advances in new
sensors and the complexity of the scenarios in emerging mobile robotics applications. The scan
matching algorithm aims to find a pose vector representing the transformation or movement
between two robot observations by finding the best possible value after solving an equation
representing a good transformation. It means searching for a solution in an optimum way. Typically
this optimization process has been solved using classical optimization algorithms, like
Newton’s algorithm or solving gradient and second derivatives formulations, yet this requires
an initial guess or initial state that helps the algorithm point in the right direction, most of the
time by getting this information from the odometers or inertial sensors. Although, it is not always possible to have or trust this information, as some scenarios are complex and reckon
sensors fail. In order to solve this problem, this research presents the uses of evolutionary optimization
algorithms, those with a meta-heuristics definition based on iterative evolution that
mimics optimization processes that do not need previous information to search a limited range
for solutions to solve a fitness function. The main goal of this dissertation is to study, develop
and prove the benefits of evolutionary optimization algorithms in simultaneous localization and
mapping for mobile robots in six degrees of freedom scenarios using LiDAR sensor information.
This work introduces several evolutionary algorithms for scan matching, acknowledge a
mixed fitness function for registration, solve simultaneous localization and matching in different
scenarios, implements loop closure and error relaxation, and proves its performance at indoors,
outdoors and underground mapping applications.Los robots móviles están creciendo en aplicaciones para moverse por entornos interiores
y exteriores, pasando de aplicaciones teleoperadas a aplicaciones autónomas como explorar o
navegar. Para que un robot se mueva a través de una ubicación en particular, necesita recopilar
información sobre el escenario utilizando sensores. Estos sensores permiten que el robot observe,
según el tipo de datos del sensor. Las cámaras en su mayoría brindan información en
dos dimensiones, con colores y píxeles que representan una imagen. Los sensores de rango dan
distancias desde el robot hasta los obstáculos. Las Cámaras de Profundidad mezclan ambas
tecnologías para expandir su información a información tridimensional. Light Detection and
Ranging (LiDAR) proporciona información sobre la distancia al sensor, pero amplía su rango a
planos y tres dimensiones así como mejora la precisión. Por lo tanto, los robots móviles usan
esos sensores para escanear el escenario mientras se mueven. Si el robot ya tiene un mapa, los
sensores miden y el robot encuentra características que corresponden a características en dicho
mapa para localizarse. La humanidad ha utilizado los mapas como una forma especializada
de representar el medio ambiente durante más de 5000 años, convirtiéndose en una pieza de
información importante en los usos básicos diarios de hoy en día. Los mapas se utilizan para
navegar de un lugar a otro, localizar algo dentro de algunos límites o como una forma de documentación
de características esenciales. Entonces, naturalmente, una forma intuitiva de hacer
un robot móvil autónomo es implementar mapas de información geométrica para representar el
entorno. Por otro lado, si el robot no tiene un mapa previo, deberá construirlo mientras se desplaza.
El robot junta la información del sensor de distancias con la información del sensor del
odómetro para lograr esta tarea de crear un mapa. Sin embargo, los sensores tienen sus propios
defectos debido a la precisión, la calibración o la exactitud. Además, mover un robot tiene sus
limitaciones físicas y fallas que pueden ocurrir aleatoriamente, como el desvío de las ruedas o
una mala calibración mecánica que puede hacer que los contadores de desplazamiento fallen en
la medición, lo que provoca una desalineación durante la construcción del mapa. A mediados
de los años 90 se presentó una técnica novedosa para resolver este problema y superar la incertidumbre
de los sensores mientras el robot construye el mapa, el algoritmo de localización y
mapeo simultáneos (SLAM). Su objetivo es construir un mapa mientras se corrige la posición
del robot en base a la información de dos o más escaneos consecutivos emparejados o encontrar
el vector de correspondencia entre ellos. Este algoritmo ha sido ampliamente estudiado y
desarrollado durante casi 25 años. No obstante, es muy relevante en innovaciones, modificaciones
y adaptaciones debido a los avances en sensores y la complejidad de los escenarios en las
aplicaciones emergentes de robótica móvil. El algoritmo de correspondencia de escaneo tiene
como objetivo encontrar un vector de pose que represente la transformación o el movimiento
entre dos observaciones del robot al encontrar el mejor valor posible después de resolver una
ecuación que represente una buena transformación. Significa buscar una solución de forma óptima. Por lo general, este proceso de optimización se ha resuelto utilizando algoritmos de
optimización clásicos, como el algoritmo de Newton o la resolución de formulaciones de gradientes
y segundas derivadas, pero esto requiere una conjetura inicial o un estado inicial que
ayude al algoritmo a apuntar en la dirección correcta, la mayoría de las veces obteniendo esta
información de los sensores odometricos o sensores de inercia, aunque no siempre es posible
tener o confiar en esta información, ya que algunos escenarios son complejos y los sensores
fallan. Para resolver este problema, esta investigación presenta los usos de los algoritmos de
optimización evolutiva, aquellos con una definición meta-heurística basada en la evolución iterativa
que imita los procesos de optimización que no necesitan información previa para buscar
dentro de un rango limitado el grupo de soluciones que resuelve una función de calidad. El
objetivo principal de esta tesis es estudiar, desarrollar y probar los usos de algoritmos de optimización
evolutiva en localización y mapeado simultáneos para robots móviles en escenarios de
seis grados de libertad utilizando información de sensores LiDAR. Este trabajo introduce varios
algoritmos evolutivos que resuelven la correspondencia entre medidas, soluciona el problema
de SLAM, implementa una fusion de funciones objetivos y demuestra sus ventajas con pruebas
en escenarios reales tanto en interiores, exteriores como mapeado de escenarios subterraneos.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Gerardo Fernández López.- Secretario: María Dolores Blanco Rojas.- Vocal: David Álvarez Sánche
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