Robust Extrinsic Self-Calibration of Camera and Solid State LiDAR

This letter proposes an extrinsic calibration approach for a pair of monocular camera and prism-spinning solid-state LiDAR. The unique characteristics of the point cloud measured resulting from the flower-like scanning pattern is first disclosed as the vacant points, a type of outlier between foreground target and background objects. Unlike existing method using only depth continuous measurements, we use depth discontinuous measurements to retain more valid features and efficiently remove vacant points. The larger number of detected 3D corners thus contain more robust a priori information than usual which, together with the 2D corners detected by overlapping cameras and constrained by the proposed circularity and rectangularity rules, produce accurate extrinsic estimates. The algorithm is evaluated with real field experiments adopting both qualitative and quantitative performance criteria, and found to be superior to existing algorithms. The code is available on GitHub

Automatic laser and camera extrinsic calibration for data fusion using road plane

Driving Assistance Systems and Autonomous Driving applications require trustable detections. These demanding requirements need sensor fusion to provide information reliable enough. But data fusion presents the problem of data alignment in both rotation and translation. Laser scanner and video cameras are widely used in sensor fusion. Laser provides operation in darkness, long range detection and accurate measurement but lacks the means for reliable classification due to the limited information provided. The camera provides classification thanks to the amount of data provided but lacks accuracy for measurements and is sensitive to illumination conditions. Data alignment processes require supervised and accurate measurements, that should be performed by experts, or require specific patterns or shapes. This paper presents an algorithm for inter-calibration between the two sensors of our system, requiring only a flat surface for pitch and roll calibration and an obstacle visible for both sensors for determining the yaw. The advantage of this system is that it does not need any particular shape to be located in front of the vehicle apart from a flat surface, which is usually the road. This way, calibration can be achieved at virtually any time without human intervention.This work was supported by Automation Engineering Department from de La Salle University, Bogotá-Colombia; Administrative Department of Science, Technology and Innovation (COLCIENCIAS), Bogotá-Colombia and the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01) and (GRANT TRA 2011-29454- C03-02)

Calibração automática de múltiplos LIDARs e câmaras usando uma esfera em movimento

Mestrado em Engenharia MecânicaVeículos autónomos têm atraído muito interesse nos últimos anos devido ao seu potencial impacto na sociedade, o que tem impulsionado esta área para estudos e desenvolvimentos constantes. Uma vez que os sistemas de perceção são extremamente importantes na navegação autónoma, a sua complexidade leva a um incremento do número de sensores a bordo (composto normalmente por sensores LIDAR, câmaras entre outros) juntamente com o aumento da sua diversidade, o que aumenta a preocupação sobre a calibração de sensores. Os métodos de calibração são normalmente manuais ou semi-automáticos e requerem intervenção de um utilizador. Poucos métodos automáticos estão disponíveis, e mesmo os que existem são normalmente baseados em processos complexos e dispositivos dispendiosos. Este trabalho apresenta um novo método de calibração automático usando uma bola como alvo para extrair correspondências entre sensores. O processo de calibração consiste em mover a bola permitindo a deteção do seu centro ao longo de sucessivas posições por todos os sensores a serem calibrados. Este estudo envolve a calibração de sensores LIDAR 2D e 3D, e câmaras. A segmentação em 2D usa um algoritmo baseado nas propriedades geométricas de um arco. Em 3D, a Point Cloud Library (PCL) sample consensus module é usado para identi car e localizar a bola. Finalmente, OpenCV é usado para calibrar o sistema stereo e computar a imagem de disparidade e a sua re-projeção 3D, resultando numa nuvem de pontos 3D. Durante o movimento da bola, é criada uma nuvem de pontos dos centros da bola para cada sensor. Finalmente, cada nuvem de pontos é alinhada com um sensor de referência. O resultado nal do processo é a transformação de corpo rígido de cada sensor com respeito ao sensor de referência. O método foi testado quer em laboratório quer com um veículo em tamanho real (AtlasCar). As relativas calibrações entre sensores assegura muito bons resultados que são avaliados pela consistência da performance da deteção por todos os sensores calibrados. Outra característica adicional nesta solução é a sua exibilidade ao permitir a calibração de diferentes LIDARs e câmaras.Autonomous vehicles have attracted great interest in the past years due to their potential impact on society, which has been pushing this area into continuously study and development. Since the perception systems are extremely important in autonomous navigation, their complexity leads to an increment of the number of sensors on board (composed commonly by LIDAR, cameras and other sensors) along with the increase of their diversity, which raised concerns about sensor calibration. Calibration methods are usually manual or semi-automatic and require user intervention. Few automatic methods are available, and even the existent methods are normally based in complex processes and expensive devices. This work presents a new automatic calibration method using a ball as target to extract correspondences between sensors. The process of calibration consists of moving the ball allowing the detection of its center along successive positions by all the sensors to be calibrated. This study involves the calibration of 2D and 3D LIDAR sensors, and cameras. Segmentation in 2D uses an algorithm based on the geometric properties of an arc. In 3D, the Point Cloud Library (PCL) sample consensus module is used to identify and locate the ball. Finally, OpenCV is used to calibrate a stereo system and compute the disparity image and its 3D re-projection, resulting in a 3D point cloud. During ball motion, a point cloud of the ball centers is created for each sensor. Finally, all the point clouds are aligned with a reference sensor. The nal result of the process is the rigid body transformation of each sensor with respect to the reference frame. The method was tested both in laboratory experiments and in a real full size vehicle (AtlasCar). The relative calibration among all sensors yields very good results that are evaluated by the consistency of the detection performed by the calibrated sensors. Another additional feature of this solution is its exibility by permitting the calibration of several di erent LIDARs and cameras

Multisensory 4D phenotyping platform in rice

La agricultura tiene la necesidad de producir cultivos de alto rendimiento, los cuales deben adaptarse al cambio climático y a las diferentes plagas que los atacan en su producción. Para esto, la fenómica ofrece un conjunto de nuevas tecnologías que aceleran el progreso en la comprensión de las características resultantes entre la interacción de genes y condiciones ambientales que rodean a los cultivos, y de esta manera, poder realizar un mejoramiento del mismo. En la fenómica hay dos actores principales necesarios para lograr dicho mejoramiento, uno de ellos es el genotipado y el otro el fenotipado. Dentro del fenotipado, la morfología de las plantas es uno de los tipos más importantes de rasgos a medir, ya que proporciona una forma factible de evaluar el crecimiento, la fisiolog ía, el estrés, el rendimiento y cada desarrollo en la planta. Además, es fundamental para mejorar la caracterización, selección y discriminación de las mismas. Es por eso, que en este proyecto, mediante la fusión sensorial de un LiDAR y una cámara multiespectral, se diseñó e implementó una plataforma de fenotipado que permite extraer y visualizar diferentes caracter ́ısticas morfolo ́gicas de plantas mediante un modelo tridimensional. Para evaluar los resultados obtenidos se realizaron pruebas con dos tipos de plantas que contaban con diferente estructuras en su follaje, donde se evaluó la fidelidad de la reconstrucción 3D y las características morfológicas obtenidas. De lo anterior se encontró que la fusión de información proveniente de sensores 3D y 2D permite generar modelos 4D, que pueden ayudar a la extracción y el entendimiento de características morfológicas en plantas.OMICAS: Optimización Multiescala In-silico de Cultivos Agrícolas SosteniblesAgriculture has the need to produce high-yielding crops, which must adapt to climate change and to the different pests that attack them in their production. For this, phenomics offers a set of new technologies that accelerate progress in understanding the characteristics resulting from the interaction between genes and environmental conditions surrounding crops, and in this way, to be able to improve them. In phenomics there are two main actors necessary to achieve such improvement, one of them is genotyping and the other is phenotyping.Within phenotyping, plant morphology is one of the most important types of traits to mea- sure, as it provides a feasible way to evaluate growth, physiology, stress, yield and every development in the plant. In addition, it is critical for improving plant characterization, selection and discrimination.Therefore, in this project, through the sensory fusion of a LiDAR and a multispectral camera, a phenotyping platform was designed and implemented to extract and visualize different morphological characteristics of plants through a three-dimensional model.To evaluate the results obtained, tests were carried out with two types of plants with dif- ferent foliage structures, where the fidelity of the 3D reconstruction and the morphological characteristics obtained were evaluated.From the above, it was found that the fusion of information from 3D and 2D sensors allows the generation of 4D models, which can help in the extraction and understanding of morpho- logical characteristics in plants.Magíster en Ingeniería ElectrónicaMaestrí

Fine-scale Inventory of Forest Biomass with Ground-based LiDAR

Biomass measurement provides a baseline for ecosystem valuation required by modern forest management. The advent of ground-based LiDAR technology, renowned for 3D sampling resolution, has been altering the routines of biomass inventory. The thesis develops a set of innovative approaches in support of fine-scale biomass inventory, including automatic extraction of stem statistics, robust delineation of plot biomass components, accurate classification of individual tree species, and repeatable scanning of plot trees using a lightweight scanning system. Main achievements in terms of accuracy are a relative root mean square error of 11% for stem volume extraction, a mean classification accuracy of 0.72 for plot wood components, and a classification accuracy of 92% among seven tree species. The results indicate the technical feasibility of biomass delineation and monitoring from plot-level and multi-species point cloud datasets, whereas point occlusion and lack of fine-scale validation dataset are current challenges for biomass 3D analysis from ground.S.G.S. International Tuition Award from the University of Lethbridge The Dean's Scholarship from the University of Lethbridge Campus Alberta Innovates Program NSERC Discovery Grants Progra

Manipulador aéreo con brazos antropomórficos de articulaciones flexibles

[Resumen] Este artículo presenta el primer robot manipulador aéreo con dos brazos antropomórficos diseñado para aplicarse en tareas de inspección y mantenimiento en entornos industriales de difícil acceso para operarios humanos. El robot consiste en una plataforma aérea multirrotor equipada con dos brazos antropomórficos ultraligeros, así como el sistema de control integrado de la plataforma y los brazos. Una de las principales características del manipulador es la flexibilidad mecánica proporcionada en todas las articulaciones, lo que aumenta la seguridad en las interacciones físicas con el entorno y la protección del propio robot. Para ello se ha introducido un compacto y simple mecanismo de transmisión por muelle entre el eje del servo y el enlace de salida. La estructura en aluminio de los brazos ha sido cuidadosamente diseñada de forma que los actuadores estén aislados frente a cargas radiales y axiales que los puedan dañar. El manipulador desarrollado ha sido validado a través de experimentos en base fija y en pruebas de vuelo en exteriores.Ministerio de Economía y Competitividad; DPI2014-5983-C2-1-