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

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

Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy

Autonomous vehicles are becoming central for the future of mobility, supported by advances in deep learning techniques. The performance of aself-driving system is highly dependent on the quality of the perception task. Developments in sensor technologies have led to an increased availability of 3D scanners such as LiDAR, allowing for a more accurate representation of the vehicle's surroundings, leading to safer systems. The rapid development and consequent rise of research studies around self-driving systems since early 2010, resulted in a tremendous increase in the number and novelty of object detection methods. After the first wave of works that essentially tried to expand known techniques from object detection in images, more recently there has been a notable development in newer and more adapted to LiDAR data works. This paper addresses the existing literature on object detection using LiDAR data within the scope of self-driving and brings a systematic way for analysing it. Unlike general object detection surveys, we will focus on point-cloud data, which presents specific challenges, notably its high-dimensional and sparse nature. This work introduces a common object detection pipeline and taxonomy to facilitate a thorough comparison between different techniques and, departing from it, this work will critically examine the representation of data (critical for complexity reduction), feature extraction and finally the object detection models. A comparison between performance results of the different models is included, alongside with some future research challenges.This work is supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n. 037902; Funding Reference: POCI-01-0247-FEDER-037902]

Using AI and Robotics for EV battery cable detection.: Development and implementation of end-to-end model-free 3D instance segmentation for industrial purposes

Master's thesis in Information- and communication technology (IKT590)This thesis describes a novel method for capturing point clouds and segmenting instances of cabling found on electric vehicle battery packs. The use of cutting-edge perception algorithm architectures, such as graph-based and voxel-based convolution, in industrial autonomous lithium-ion battery pack disassembly is being investigated. The thesis focuses on the challenge of getting a desirable representation of any battery pack using an ABB robot in conjunction with a high-end structured light camera, with "end-to-end" and "model-free" as design constraints. The thesis employs self-captured datasets comprised of several battery packs that have been captured and labeled. Following that, the datasets are used to create a perception system. This thesis recommends using HDR functionality in an industrial application to capture the full dynamic range of the battery packs. To adequately depict 3D features, a three-point-of-view capture sequence is deemed necessary. A general capture process for an entire battery pack is also presented, but a next-best-scan algorithm is likely required to ensure a "close to complete" representation. Graph-based deep-learning algorithms have been shown to be capable of being scaled up to50,000inputs while still exhibiting strong performance in terms of accuracy and processing time. The results show that an instance segmenting system can be implemented in less than two seconds. Using off-the-shelf hardware, demonstrate that a 3D perception system is industrially viable and competitive with a 2D perception system

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Learning Birds-Eye View Representations for Autonomous Driving

Over the past few years, progress towards the ambitious goal of widespread fully-autonomous vehicles on our roads has accelerated dramatically. This progress has been spurred largely by the success of highly accurate LiDAR sensors, as well the use of detailed high-resolution maps, which together allow a vehicle to navigate its surroundings effectively. Often, however, one or both of these resources may be unavailable, whether due to cost, sensor failure, or the need to operate in an unmapped environment. The aim of this thesis is therefore to demonstrate that it is possible to build detailed three-dimensional representations of traffic scenes using only 2D monocular camera images as input. Such an approach faces many challenges: most notably that 2D images do not provide explicit 3D structure. We overcome this limitation by applying a combination of deep learning and geometry to transform image-based features into an orthographic birds-eye view representation of the scene, allowing algorithms to reason in a metric, 3D space. This approach is applied to solving two challenging perception tasks central to autonomous driving. The first part of this thesis addresses the problem of monocular 3D object detection, which involves determining the size and location of all objects in the scene. Our solution was based on a novel convolutional network architecture that processed features in both the image and birds-eye view perspective. Results on the KITTI dataset showed that this network outperformed existing works at the time, and although more recent works have improved on these results, we conducted extensive analysis to find that our solution performed well in many difficult edge-case scenarios such as objects close to or distant from the camera. In the second part of the thesis, we consider the related problem of semantic map prediction. This consists of estimating a birds-eye view map of the world visible from a given camera, encoding both static elements of the scene such as pavement and road layout, as well as dynamic objects such as vehicles and pedestrians. This was accomplished using a second network that built on the experience from the previous work and achieved convincing performance on two real-world driving datasets. By formulating the maps as an occupancy grid map (a widely used representation from robotics), we were able to demonstrate how predictions could be accumulated across multiple frames, and that doing so further improved the robustness of maps produced by our system.Toyota Motors Europ