2,740 research outputs found
3D Segmentation Method for Natural Environments based on a Geometric-Featured Voxel Map
This work proposes a new segmentation algorithm for three-dimensional dense point clouds and has been
specially designed for natural environments where the ground is unstructured and may include big slopes, non-flat areas and
isolated areas. This technique is based on a Geometric-Featured Voxel map (GFV) where the scene is discretized in
constant size cubes or voxels which are classified in flat surface, linear or tubular structures and scattered or undefined
shapes, usually corresponding to vegetation. Since this is not a point-based technique the computational cost is significantly
reduced, hence it may be compatible with Real-Time applications. The ground is extracted in order to obtain more accurate
results in the posterior segmentation process. The scene is split into objects and a second segmentation in regions inside
each object is performed based on the voxel’s geometric class. The work here evaluates the proposed algorithm in various
versions and several voxel sizes and compares the results with other methods from the literature. For the segmentation
evaluation the algorithms are tested on several differently challenging hand-labeled data sets using two metrics, one of which
is novel.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation
Autonomous harvesting and transportation is a long-term goal of the forest
industry. One of the main challenges is the accurate localization of both
vehicles and trees in a forest. Forests are unstructured environments where it
is difficult to find a group of significant landmarks for current fast
feature-based place recognition algorithms. This paper proposes a novel
approach where local observations are matched to a general tree map using the
Delaunay triangularization as the representation format. Instead of point cloud
based matching methods, we utilize a topology-based method. First, tree trunk
positions are registered at a prior run done by a forest harvester. Second, the
resulting map is Delaunay triangularized. Third, a local submap of the
autonomous robot is registered, triangularized and matched using triangular
similarity maximization to estimate the position of the robot. We test our
method on a dataset accumulated from a forestry site at Lieksa, Finland. A
total length of 2100\,m of harvester path was recorded by an industrial
harvester with a 3D laser scanner and a geolocation unit fixed to the frame.
Our experiments show a 12\,cm s.t.d. in the location accuracy and with
real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and
speed limit is realistic during forest operations
Indoor assistance for visually impaired people using a RGB-D camera
In this paper a navigational aid for visually impaired people is presented. The system uses a RGB-D camera to perceive the environment and implements self-localization, obstacle detection and obstacle classification. The novelty of this work is threefold. First, self-localization is performed by means of a novel camera tracking approach that uses both depth and color information. Second, to provide the user with semantic information, obstacles are classified as walls, doors, steps and a residual class that covers isolated objects and bumpy parts on the floor. Third, in order to guarantee real time performance, the system is accelerated by offloading parallel operations to the GPU. Experiments demonstrate that the whole system is running at 9 Hz
Contributions to Intelligent Scene Understanding of Unstructured Environments from 3D lidar sensors
Además, la viabilidad de este enfoque es evaluado mediante la implementación de cuatro tipos de clasificadores de aprendizaje supervisado encontrados en métodos de procesamiento de escenas: red neuronal, máquina de vectores de soporte, procesos gaussianos, y modelos de mezcla gaussiana.
La segmentación de objetos es un paso más allá hacia el entendimiento de escena, donde conjuntos de puntos 3D correspondientes al suelo y otros objetos de la escena son aislados. La tesis propone nuevas contribuciones a la segmentación de nubes de puntos basados en mapas de vóxeles caracterizados geométricamente. En concreto, la metodología propuesta se compone de dos pasos: primero, una segmentación del suelo especialmente diseñado para entornos naturales; y segundo, el posterior aislamiento de objetos individuales. Además, el método de segmentación del suelo es integrado en una nueva técnica de mapa de navegabilidad basado en cuadrícula de ocupación el cuál puede ser apropiado para robots móviles en entornos naturales.
El diseño y desarrollo de un nuevo y asequible sensor lidar 3D de alta resolución también se ha propuesto en la tesis. Los nuevos MBLs, tales como los desarrollados por Velodyne, están siendo cada vez más un tipo de sensor 3D asequible y popular que ofrece alto ratio de datos en un campo de visión vertical (FOV) limitado. El diseño propuesto consiste en una plataforma giratoria que mejora la resolución y el FOV vertical de un Velodyne VLP-16 de 16 haces. Además, los complejos patrones de escaneo producidos por configuraciones de MBL que rotan se analizan tanto en simulaciones de esfera hueca como en escáneres reales en entornos representativos.
Fecha de Lectura de Tesis: 11 de julio 2018.Ingeniería de Sistemas y Automática
Resumen tesis:
Los sensores lidar 3D son una tecnología clave para navegación, localización, mapeo y entendimiento de escenas en vehículos no tripulados y robots móviles.
Esta tecnología, que provee nubes de puntos densas, puede ser especialmente adecuada para nuevas aplicaciones en entornos naturales o desestructurados, tales como búsqueda y rescate, exploración planetaria, agricultura, o exploración fuera de carretera.
Esto es un desafío como área de investigación que incluye disciplinas que van desde el diseño de sensor a la inteligencia artificial o el aprendizaje automático (machine learning). En este contexto, esta tesis propone contribuciones al entendimiento inteligente de escenas en entornos desestructurados basado en medidas 3D de distancia a nivel del suelo. En concreto, las contribuciones principales incluyen nuevas metodologías para la clasificación de características espaciales, segmentación de objetos, y evaluación de navegabilidad en entornos naturales y urbanos, y también el diseño y desarrollo de un nuevo lidar rotatorio multi-haz (MBL).
La clasificación de características espaciales es muy relevante porque es extensamente requerida como un paso fundamental previo a los problemas de entendimiento de alto nivel de una escena. Las contribuciones de la tesis en este respecto tratan de mejorar la eficacia, tanto en carga computacional como en precisión, de clasificación de aprendizaje supervisado de características de forma espacial (forma tubular, plana o difusa) obtenida mediante el análisis de componentes principales (PCA). Esto se ha conseguido proponiendo un concepto eficiente de vecindario basado en vóxel en una contribución original que define los procedimientos de aprendizaje “offline” y clasificación “online” a la vez que cinco definiciones alternativas de vectores de características basados en PCA
A New Approach for Realistic 3D Reconstruction of Planar Surfaces from Laser Scanning Data and Imagery Collected Onboard Modern Low-Cost Aerial Mapping Systems
Over the past few years, accurate 3D surface reconstruction using remotely-sensed data has been recognized as a prerequisite for different mapping, modelling, and monitoring applications. To fulfill the needs of these applications, necessary data are generally collected using various digital imaging systems. Among them, laser scanners have been acknowledged as a fast, accurate, and flexible technology for the acquisition of high density 3D spatial data. Despite their quick accessibility, the acquired 3D data using these systems does not provide semantic information about the nature of scanned surfaces. Hence, reliable processing techniques are employed to extract the required information for 3D surface reconstruction. Moreover, the extracted information from laser scanning data cannot be effectively utilized due to the lack of descriptive details. In order to provide a more realistic and accurate perception of the scanned scenes using laser scanning systems, a new approach for 3D reconstruction of planar surfaces is introduced in this paper. This approach aims to improve the interpretability of the extracted planar surfaces from laser scanning data using spectral information from overlapping imagery collected onboard modern low-cost aerial mapping systems, which are widely adopted nowadays. In this approach, the scanned planar surfaces using laser scanning systems are initially extracted through a novel segmentation procedure, and then textured using the acquired overlapping imagery. The implemented texturing technique, which intends to overcome the computational inefficiency of the previously-developed 3D reconstruction techniques, is performed in three steps. In the first step, the visibility of the extracted planar surfaces from laser scanning data within the collected images is investigated and a list of appropriate images for texturing each surface is established. Successively, an occlusion detection procedure is carried out to identify the occluded parts of these surfaces in the field of view of captured images. In the second step, visible/non-occluded parts of the planar surfaces are decomposed into segments that will be textured using individual images. Finally, a rendering procedure is accomplished to texture these parts using available images. Experimental results from overlapping laser scanning data and imagery collected onboard aerial mapping systems verify the feasibility of the proposed approach for efficient realistic 3D surface reconstruction
Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard
This paper presents a novel method for fully automatic and convenient
extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally
printed chessboard. The proposed method is based on the 3D corner estimation of
the chessboard from the sparse point cloud generated by one frame scan of the
LiDAR. To estimate the corners, we formulate a full-scale model of the
chessboard and fit it to the segmented 3D points of the chessboard. The model
is fitted by optimizing the cost function under constraints of correlation
between the reflectance intensity of laser and the color of the chessboard's
patterns. Powell's method is introduced for resolving the discontinuity problem
in optimization. The corners of the fitted model are considered as the 3D
corners of the chessboard. Once the corners of the chessboard in the 3D point
cloud are estimated, the extrinsic calibration of the two sensors is converted
to a 3D-2D matching problem. The corresponding 3D-2D points are used to
calculate the absolute pose of the two sensors with Unified Perspective-n-Point
(UPnP). Further, the calculated parameters are regarded as initial values and
are refined using the Levenberg-Marquardt method. The performance of the
proposed corner detection method from the 3D point cloud is evaluated using
simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR
and a Ladybug3 camera under the proposed re-projection error metric,
qualitatively and quantitatively demonstrate the accuracy and stability of the
final extrinsic calibration parameters.Comment: 20 pages, submitted to the journal of Remote Sensin
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