On the Mahalanobis Distance Classification Criterion for Multidimensional Normal Distributions

Many existing engineering works model the statistical characteristics of the entities under study as normal distributions. These models are eventually used for decision making, requiring in practice the definition of the classification region corresponding to the desired confidence level. Surprisingly enough, however, a great amount of computer vision works using multidimensional normal models leave unspecified or fail to establish correct confidence regions due to misconceptions on the features of Gaussian functions or to wrong analogies with the unidimensional case. The resulting regions incur in deviations that can be unacceptable in high-dimensional models. Here we provide a comprehensive derivation of the optimal confidence regions for multivariate normal distributions of arbitrary dimensionality. To this end, firstly we derive the condition for region optimality of general continuous multidimensional distributions, and then we apply it to the widespread case of the normal probability density function. The obtained results are used to analyze the confidence error incurred by previous works related to vision research, showing that deviations caused by wrong regions may turn into unacceptable as dimensionality increases. To support the theoretical analysis, a quantitative example in the context of moving object detection by means of background modeling is given.

AR drone identification and navigation control at CVG-UPM

This article presents the proposal of the Computer Vision Group to the first phase of the international competition “Concurso de Ingeniería de Control 2012, Control Aut ́onomo del seguimiento de trayectorias de un vehículo cuatrirrotor”. This phase consists mainly of two parts: identifying a model and designing a trajectory controller for the AR Drone quadrotor. For the identification task, two models are proposed: a simplified model that captures only the main dynamics of the quadrotor, and a second model based on the physical laws underlying the AR Drone behavior. The trajectory controller design is based on the simplified model, whereas the physical model is used to tune the controller to attain a certain level of robust stability to model uncertainties. The controller design is simplified by the hypothesis that accurate positions sensors will be available to implement a feedback controller.

Visual Tracking, Pose Estimation, and Control for Aerial Vehicles

El principal objetivo de esta tesis es dotar a los vehículos aéreos no tripulados (UAVs, por sus siglas en inglés) de una fuente de información adicional basada en visión. Esta fuente de información proviene de cámaras ubicadas a bordo de los vehículos o en el suelo. Con ella se busca que los UAVs realicen tareas de aterrizaje o inspección guiados por visión, especialmente en aquellas situaciones en las que no haya disponibilidad de estimar la posición del vehículo con base en GPS, cuando las estimaciones de GPS no tengan la suficiente precisión requerida por las tareas a realizar, o cuando restricciones de carga de pago impidan añadir sensores a bordo de los vehículos. Esta tesis trata con tres de las principales áreas de la visión por computador: seguimiento visual y estimación visual de la pose (posición y orientación), que a su vez constituyen la base de la tercera, denominada control servo visual, que en nuestra aplicación se enfoca en el empleo de información visual para controlar los UAVs. Al respecto, esta tesis se ocupa de presentar propuestas novedosas que permitan solucionar problemas relativos al seguimiento de objetos mediante cámaras ubicadas a bordo de los UAVs, se ocupa de la estimación de la pose de los UAVs basada en información visual obtenida por cámaras ubicadas en el suelo o a bordo, y también se ocupa de la aplicación de las técnicas propuestas para solucionar diferentes problemas, como aquellos concernientes al seguimiento visual para tareas de reabastecimiento autónomo en vuelo o al aterrizaje basado en visión, entre otros. Las diversas técnicas de visión por computador presentadas en esta tesis se proponen con el fin de solucionar dificultades que suelen presentarse cuando se realizan tareas basadas en visión con UAVs, como las relativas a la obtención, en tiempo real, de estimaciones robustas, o como problemas generados por vibraciones. Los algoritmos propuestos en esta tesis han sido probados con información de imágenes reales obtenidas realizando pruebas on-line y off-line. Diversos mecanismos de evaluación han sido empleados con el propósito de analizar el desempeño de los algoritmos propuestos, entre los que se incluyen datos simulados, imágenes de vuelos reales, estimaciones precisas de posición empleando el sistema VICON y comparaciones con algoritmos del estado del arte. Los resultados obtenidos indican que los algoritmos de visión por computador propuestos tienen un desempeño que es comparable e incluso mejor al de algoritmos que se encuentran en el estado del arte. Los algoritmos propuestos permiten la obtención de estimaciones robustas en tiempo real, lo cual permite su uso en tareas de control visual. El desempeño de estos algoritmos es apropiado para las exigencias de las distintas aplicaciones examinadas: reabastecimiento autónomo en vuelo, aterrizaje y estimación del estado del UAV. Abstract The main objective of this thesis is to provide Unmanned Aerial Vehicles (UAVs) with an additional vision-based source of information extracted by cameras located either on-board or on the ground, in order to allow UAVs to develop visually guided tasks, such as landing or inspection, especially in situations where GPS information is not available, where GPS-based position estimation is not accurate enough for the task to develop, or where payload restrictions do not allow the incorporation of additional sensors on-board. This thesis covers three of the main computer vision areas: visual tracking and visual pose estimation, which are the bases the third one called visual servoing, which, in this work, focuses on using visual information to control UAVs. In this sense, the thesis focuses on presenting novel solutions for solving the tracking problem of objects when using cameras on-board UAVs, on estimating the pose of the UAVs based on the visual information collected by cameras located either on the ground or on-board, and also focuses on applying these proposed techniques for solving different problems, such as visual tracking for aerial refuelling or vision-based landing, among others. The different computer vision techniques presented in this thesis are proposed to solve some of the frequently problems found when addressing vision-based tasks in UAVs, such as obtaining robust vision-based estimations at real-time frame rates, and problems caused by vibrations, or 3D motion. All the proposed algorithms have been tested with real-image data in on-line and off-line tests. Different evaluation mechanisms have been used to analyze the performance of the proposed algorithms, such as simulated data, images from real-flight tests, publicly available datasets, manually generated ground truth data, accurate position estimations using a VICON system and a robotic cell, and comparison with state of the art algorithms. Results show that the proposed computer vision algorithms obtain performances that are comparable to, or even better than, state of the art algorithms, obtaining robust estimations at real-time frame rates. This proves that the proposed techniques are fast enough for vision-based control tasks. Therefore, the performance of the proposed vision algorithms has shown to be of a standard appropriate to the different explored applications: aerial refuelling and landing, and state estimation. It is noteworthy that they have low computational overheads for vision systems.

Detection and Tracking of Dynamic Objects. A MultiRobot approach to Critical Infrastructures Surveillance.

Despite that Critical Infrastructures (CIs) security and surveillance are a growing concern for many countries and companies, Multi Robot Systems (MRSs) have not been yet broadly used in this type of facilities. This dissertation presents a novel study of the challenges arisen by the implementation of this type of systems and proposes solutions to specific problems. First, a comprehensive analysis of different types of CIs has been carried out, emphasizing the influence of the different characteristics of the facilities in the design of a security and surveillance MRS. One of the most important needs for the surveillance of a CI is the detection of intruders. From a technical point of view this problem can be abstracted as equivalent to the Detection and Tracking of Mobile Objects (DATMO). This dissertation proposes algorithms to solve this specific problem in a CI environment. Using 3D range images of the environment as input data, two detection algorithms for ground robots have been developed. These detection algorithms provide a list of moving objects in the robot detection area. Direct image differentiation and computer vision techniques are used when the robot is static. Alternatively, multi-layer ground reconstructions are compared to detect the dynamic objects when the robot is moving. Since CIs usually spread over large areas, it is very useful to incorporate aerial vehicles in the surveillance MRS. Therefore, a moving object detection algorithm for aerial vehicles has been also developed. This algorithm compares the real optical flow obtained from a down-face oriented camera with an artificial optical flow computed using a RANSAC based homography matrix. Two tracking algorithms have been developed to follow the moving objects trajectories. These algorithms can efficiently handle occlusions and crossings, as well as exchange information among robots. The multirobot tracking can be applied to any type of communication structure: centralized, decentralized or a combination of both. Even more, the developed tracking algorithms are independent of the detection algorithms and could be potentially used with other detection procedures or even with static sensors, such as cameras. In addition, using the 3D point clouds available to the robots, a relative localization algorithm has been developed to improve the position estimation of a given robot with observations from other robots. All the developed algorithms have been extensively tested in different simulated CIs using the Webots robotics simulator. Furthermore, the algorithms have also been validated with real robots operating in real scenarios. In conclusion, this dissertation presents a multirobot approach to Critical Infrastructure Surveillance, mainly focusing on Detecting and Tracking Dynamic Objects.

Multi-resolution model-based traffic sign detection and tracking

In this paper we propose an innovative approach to tackle the problem of traffic sign detection using a computer vision algorithm and taking into account real-time operation constraints, trying to establish intelligent strategies to simplify as much as possible the algorithm complexity and to speed up the process. Firstly, a set of candidates is generated according to a color segmentation stage, followed by a region analysis strategy, where spatial characteristic of previously detected objects are taken into account. Finally, temporal coherence is introduced by means of a tracking scheme, performed using a Kalman filter for each potential candidate. Taking into consideration time constraints, efficiency is achieved two-fold: on the one side, a multi-resolution strategy is adopted for segmentation, where global operation will be applied only to low-resolution images, increasing the resolution to the maximum only when a potential road sign is being tracked. On the other side, we take advantage of the expected spacing between traffic signs. Namely, the tracking of objects of interest allows to generate inhibition areas, which are those ones where no new traffic signs are expected to appear due to the existence of a TS in the neighborhood. The proposed solution has been tested with real sequences in both urban areas and highways, and proved to achieve higher computational efficiency, especially as a result of the multi-resolution approach.

Computer analysis and design of concrete shell roofs

This paper is a preliminary version of Chapter 3 of a State-of-the-Art Report by the IASS Working Group 5: Concrete Shell Roofs. The intention of this chapter is to set forth for those who intend to design concrete shell roofs information and advice about the selection, verification and utilization of commercial computer tools for analysis and design tasks.The computer analysis and design steps for a concrete shell roof are described. Advice follows on the aspects to be considered in the application of commercial finite element (FE)computer programs to concrete shell analysis, starting with recommendations on how novices can gain confidence and competence in the use of software. To establish vocabulary and provide background references, brief surveys are presented of, first,element types and formulations for shells and, second, challenges presented by advanced analyses of shells. The final section of the chapter indicates what capabilities to seek in selecting commercial FE software for the analysis and design of concrete shell roofs. Brief concluding remarks summarize advice regarding judicious use of computer analysis in design practice.

Correction of the Colour Variations under Uncontrolled Lighting Conditions

Esta tesis trata sobre métodos de corrección que compensan la variación de las condiciones de iluminación en aplicaciones de imagen y video a color. Estas variaciones hacen que a menudo fallen aquellos algoritmos de visión artificial que utilizan características de color para describir los objetos. Se formulan tres preguntas de investigación que definen el marco de trabajo de esta tesis. La primera cuestión aborda las similitudes que se dan entre las imágenes de superficies adyacentes en relación a su comportamiento fotométrico. En base al análisis del modelo de formación de imágenes en situaciones dinámicas, esta tesis propone un modelo capaz de predecir las variaciones de color de la región de una determinada imagen a partir de las variaciones de las regiones colindantes. Dicho modelo se denomina Quotient Relational Model of Regions. Este modelo es válido cuando: las fuentes de luz iluminan todas las superficies incluídas en él; estas superficies están próximas entre sí y tienen orientaciones similares; y cuando son en su mayoría lambertianas. Bajo ciertas circunstancias, la respuesta fotométrica de una región se puede relacionar con el resto mediante una combinación lineal. No se ha podido encontrar en la literatura científica ningún trabajo previo que proponga este tipo de modelo relacional. La segunda cuestión va un paso más allá y se pregunta si estas similitudes se pueden utilizar para corregir variaciones fotométricas desconocidas en una región también desconocida, a partir de regiones conocidas adyacentes. Para ello, se propone un método llamado Linear Correction Mapping capaz de dar una respuesta afirmativa a esta cuestión bajo las circunstancias caracterizadas previamente. Para calcular los parámetros del modelo se requiere una etapa de entrenamiento previo. El método, que inicialmente funciona para una sola cámara, se amplía para funcionar en arquitecturas con varias cámaras sin solape entre sus campos visuales. Para ello, tan solo se necesitan varias muestras de imágenes del mismo objeto capturadas por todas las cámaras. Además, este método tiene en cuenta tanto las variaciones de iluminación, como los cambios en los parámetros de exposición de las cámaras. Todos los métodos de corrección de imagen fallan cuando la imagen del objeto que tiene que ser corregido está sobreexpuesta o cuando su relación señal a ruido es muy baja. Así, la tercera cuestión se refiere a si se puede establecer un proceso de control de la adquisición que permita obtener una exposición óptima cuando las condiciones de iluminación no están controladas. De este modo, se propone un método denominado Camera Exposure Control capaz de mantener una exposición adecuada siempre y cuando las variaciones de iluminación puedan recogerse dentro del margen dinámico de la cámara. Los métodos propuestos se evaluaron individualmente. La metodología llevada a cabo en los experimentos consistió en, primero, seleccionar algunos escenarios que cubrieran situaciones representativas donde los métodos fueran válidos teóricamente. El Linear Correction Mapping fue validado en tres aplicaciones de re-identificación de objetos (vehículos, caras y personas) que utilizaban como caracterísiticas la distribución de color de éstos. Por otra parte, el Camera Exposure Control se probó en un parking al aire libre. Además de esto, se definieron varios indicadores que permitieron comparar objetivamente los resultados de los métodos propuestos con otros métodos relevantes de corrección y auto exposición referidos en el estado del arte. Los resultados de la evaluación demostraron que los métodos propuestos mejoran los métodos comparados en la mayoría de las situaciones. Basándose en los resultados obtenidos, se puede decir que las respuestas a las preguntas de investigación planteadas son afirmativas, aunque en circunstancias limitadas. Esto quiere decir que, las hipótesis planteadas respecto a la predicción, la corrección basada en ésta y la auto exposición, son factibles en aquellas situaciones identificadas a lo largo de la tesis pero que, sin embargo, no se puede garantizar que se cumplan de manera general. Por otra parte, se señalan como trabajo de investigación futuro algunas cuestiones nuevas y retos científicos que aparecen a partir del trabajo presentado en esta tesis. ABSTRACT This thesis discusses the correction methods used to compensate the variation of lighting conditions in colour image and video applications. These variations are such that Computer Vision algorithms that use colour features to describe objects mostly fail. Three research questions are formulated that define the framework of the thesis. The first question addresses the similarities of the photometric behaviour between images of dissimilar adjacent surfaces. Based on the analysis of the image formation model in dynamic situations, this thesis proposes a model that predicts the colour variations of the region of an image from the variations of the surrounded regions. This proposed model is called the Quotient Relational Model of Regions. This model is valid when the light sources illuminate all of the surfaces included in the model; these surfaces are placed close each other, have similar orientations, and are primarily Lambertian. Under certain circumstances, a linear combination is established between the photometric responses of the regions. Previous work that proposed such a relational model was not found in the scientific literature. The second question examines whether those similarities could be used to correct the unknown photometric variations in an unknown region from the known adjacent regions. A method is proposed, called Linear Correction Mapping, which is capable of providing an affirmative answer under the circumstances previously characterised. A training stage is required to determine the parameters of the model. The method for single camera scenarios is extended to cover non-overlapping multi-camera architectures. To this extent, only several image samples of the same object acquired by all of the cameras are required. Furthermore, both the light variations and the changes in the camera exposure settings are covered by correction mapping. Every image correction method is unsuccessful when the image of the object to be corrected is overexposed or the signal-to-noise ratio is very low. Thus, the third question refers to the control of the acquisition process to obtain an optimal exposure in uncontrolled light conditions. A Camera Exposure Control method is proposed that is capable of holding a suitable exposure provided that the light variations can be collected within the dynamic range of the camera. Each one of the proposed methods was evaluated individually. The methodology of the experiments consisted of first selecting some scenarios that cover the representative situations for which the methods are theoretically valid. Linear Correction Mapping was validated using three object re-identification applications (vehicles, faces and persons) based on the object colour distributions. Camera Exposure Control was proved in an outdoor parking scenario. In addition, several performance indicators were defined to objectively compare the results with other relevant state of the art correction and auto-exposure methods. The results of the evaluation demonstrated that the proposed methods outperform the compared ones in the most situations. Based on the obtained results, the answers to the above-described research questions are affirmative in limited circumstances, that is, the hypothesis of the forecasting, the correction based on it, and the auto exposure are feasible in the situations identified in the thesis, although they cannot be guaranteed in general. Furthermore, the presented work raises new questions and scientific challenges, which are highlighted as future research work.

Detección de marcas para realidad aumentada sobre dispositivos móviles

Este documento es una guía para el desarrollo de una aplicación para dispositivos móviles en Android. Dicha aplicación combina las técnicas de visión por computador para calibrar la cámara del dispositivo y localizar un elemento en el espacio en base a esos los parámetros calculados en la calibración. El diseño de la aplicación incluye las decisiones sobre la forma en que se reciben los inputs de la aplicación, que patrones se utilizan en la calibración y en la localización y como se muestran los resultados finales al usuario. También incluye un diagrama de flujo de información que representa el tránsito de esta entre los diferentes módulos. La implementación comienza con la configuración de un entorno para desarrollar aplicaciones con parte nativa en Android, después comenta el código de la aplicación paso por paso incluyendo comentarios sobre los archivos adicionales necesarios para la compilación y finalmente detalla los archivos dedicados a la interfaz. Los experimentos incluyen una breve descripción sobre cómo interpretar los resultados seguidos de una serie de imágenes tomadas de la aplicación con diferentes localizaciones del patrón. En la entrega se incluye también un video. En el capítulo de resultados y conclusiones podemos encontrar observaciones sobre el desarrollo de la práctica, opiniones sobre su utilidad, y posibles mejoras.---ABSTRACT---This document is a guide that describes the development of and application for mobile devices in Android OS. The application combines computer vision techniques to calibrate the device camera and locate an element in the real world based on the parameters of the calibration The design of the application includes the decisions over the way that the application receives its input data, the patterns used in the calibration and localization and how the results are shown to the user. It also includes a flow chart that describes how the information travels along the application modules. The development begins with the steps necessary to configure the environment to develop native Android applications, then it explains the code step by step, including commentaries on the additional files necessary to build the application and details the files of the user interface. The experiments chapter explains the way the results are shown in the experiments before showing samples of different pattern localizations. There is also a video attached. In the conclusions chapter we can find observations on the development of the TFG, opinions about its usefulness, and possibilities of improvement in the future.

Region-based moving object detection using spatially conditioned nonparametric models in a GPU

A novel GPU-based nonparametric moving object detection strategy for computer vision tools requiring real-time processing is proposed. An alternative and efficient Bayesian classifier to combine nonparametric background and foreground models allows increasing correct detections while avoiding false detections. Additionally, an efficient region of interest analysis significantly reduces the computational cost of the detections.

Real-time incidents detection in the highways of the future

Due to ever increasing transportation of people and goods, automatic traffic surveillance is becoming a key issue for both providing safety to road users and improving traffic control in an efficient way. In this paper, we propose a new system that, exploiting the capabilities that both computer vision and machine learning offer, is able to detect and track different types of real incidents on a highway. Specifically, it is able to accurately detect not only stopped vehicles, but also drivers and passengers leaving the stopped vehicle, and other pedestrians present in the roadway. Additionally, a theoretical approach for detecting vehicles which may leave the road in an unexpected way is also presented. The system works in real-time and it has been optimized for working outdoor, being thus appropriate for its deployment in a real-world environment like a highway. First experimental results on a dataset created with videos provided by two Spanish highway operators demonstrate the effectiveness of the proposed system and its robustness against noise and low-quality videos.

Multisensor data fusion for accurate modelling of mobile objects

In the last decade, multi-sensor data fusion has become a broadly demanded discipline to achieve advanced solutions that can be applied in many real world situations, either civil or military. In Defence,accurate detection of all target objects is fundamental to maintaining situational awareness, to locating threats in the battlefield and to identifying and protecting strategically own forces. Civil applications, such as traffic monitoring, have similar requirements in terms of object detection and reliable identification of incidents in order to ensure safety of road users. Thanks to the appropriate data fusion technique, we can give these systems the power to exploit automatically all relevant information from multiple sources to face for instance mission needs or assess daily supervision operations. This paper focuses on its application to active vehicle monitoring in a particular area of high density traffic, and how it is redirecting the research activities being carried out in the computer vision, signal processing and machine learning fields for improving the effectiveness of detection and tracking in ground surveillance scenarios in general. Specifically, our system proposes fusion of data at a feature level which is extracted from a video camera and a laser scanner. In addition, a stochastic-based tracking which introduces some particle filters into the model to deal with uncertainty due to occlusions and improve the previous detection output is presented in this paper. It has been shown that this computer vision tracker contributes to detect objects even under poor visual information. Finally, in the same way that humans are able to analyze both temporal and spatial relations among items in the scene to associate them a meaning, once the targets objects have been correctly detected and tracked, it is desired that machines can provide a trustworthy description of what is happening in the scene under surveillance. Accomplishing so ambitious task requires a machine learning-based hierarchic architecture able to extract and analyse behaviours at different abstraction levels. A real experimental testbed has been implemented for the evaluation of the proposed modular system. Such scenario is a closed circuit where real traffic situations can be simulated. First results have shown the strength of the proposed system.

Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Low cost RGB-D cameras such as the Microsoft’s Kinect or the Asus’s Xtion Pro are completely changing the computer vision world, as they are being successfully used in several applications and research areas. Depth data are particularly attractive and suitable for applications based on moving objects detection through foreground/background segmentation approaches; the RGB-D applications proposed in literature employ, in general, state of the art foreground/background segmentation techniques based on the depth information without taking into account the color information. The novel approach that we propose is based on a combination of classifiers that allows improving background subtraction accuracy with respect to state of the art algorithms by jointly considering color and depth data. In particular, the combination of classifiers is based on a weighted average that allows to adaptively modifying the support of each classifier in the ensemble by considering foreground detections in the previous frames and the depth and color edges. In this way, it is possible to reduce false detections due to critical issues that can not be tackled by the individual classifiers such as: shadows and illumination changes, color and depth camouflage, moved background objects and noisy depth measurements. Moreover, we propose, for the best of the author’s knowledge, the first publicly available RGB-D benchmark dataset with hand-labeled ground truth of several challenging scenarios to test background/foreground segmentation algorithms.

Joint 4-D variational stereo reconstruction and camera calibration refinement for oceanic sea state measurements

Validating modern oceanographic theories using models produced through stereo computer vision principles has recently emerged. Space-time (4-D) models of the ocean surface may be generated by stacking a series of 3-D reconstructions independently generated for each time instant or, in a more robust manner, by simultaneously processing several snapshots coherently in a true ?4-D reconstruction.? However, the accuracy of these computer-vision-generated models is subject to the estimations of camera parameters, which may be corrupted under the influence of natural factors such as wind and vibrations. Therefore, removing the unpredictable errors of the camera parameters is necessary for an accurate reconstruction. In this paper, we propose a novel algorithm that can jointly perform a 4-D reconstruction as well as correct the camera parameter errors introduced by external factors. The technique is founded upon variational optimization methods to benefit from their numerous advantages: continuity of the estimated surface in space and time, robustness, and accuracy. The performance of the proposed algorithm is tested using synthetic data produced through computer graphics techniques, based on which the errors of the camera parameters arising from natural factors can be simulated.

A General Purpose Configurable Navigation Controller for Micro Aerial Multirotor Vehicles

In this paper, we consider the problem of autonomous navigation of multirotor platforms in GPS-denied environments. The focus of this work is on safe navigation based on unperfect odometry measurements, such as on-board optical flow measurements. The multirotor platform is modeled as a flying object with specific kinematic constraints that must be taken into account in order to obtain successful results. A navigation controller is proposed featuring a set of configurable parameters that allow, for instance, to have a configuration setup for fast trajectory following, and another to soften the control laws and make the vehicle navigation more precise and slow whenever necessary. The proposed controller has been successfully implemented in two different multirotor platforms with similar sensoring capabilities showing the openness and tolerance of the approach. This research is focused around the Computer Vision Group's objective of applying multirotor vehicles to civilian service applications. The presented work was implemented to compete in the International Micro Air Vehicle Conference and Flight Competition IMAV 2012, gaining two awards: the Special Award on "Best Automatic Performance - IMAV 2012" and the second overall prize in the participating category "Indoor Flight Dynamics - Rotary Wing MAV". Most of the code related to the present work is available as two open-source projects hosted in GitHub.

Toward Visual Autonomous Ship Board Landing of a VTOL UAV

In this paper we tackle the problem of landing a helicopter autonomously on a ship deck, using as the main sensor, an on-board colour camera. To create a test-bed, we first adequately simulate the movement of a ship landing platform on the Sea, for different Sea States, for different ships, randomly and realistically enough. We use a commercial parallel robot to get this movement. Once we had this, we developed an accurate and robust computer vision system to measure the pose of the helipad with respect to the on-board camera. To deal with the noise and the possible fails of the computer vision, a state estimator was created. With all of this, we are now able to develop and test a controller that closes the loop and finish the autonomous landing task.