DISA - LSI - Capítulos de Monografías

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Autocalibración de parámetros extrínsecos de sistemas estéreo para aplicaciones de tráfico
(Universidade da Coruña, Servizo de Publicacións, 2016) Musleh Lancis, Basam; Beltrán de la Cita, Jorge; Jaraquemada Téllez, Carlos Borja; Gómez Silva, María José; Hernández Parra, Noelia; Armingol Moreno, José María; Comunidad de Madrid; Ministerio de Economía y Competitividad (España)
En este artículo se presenta un método de autocalibración de los parámetros extrínsecos de un sistema estéreo en aplicaciones de tráfico. Dicho método se basa en determinar la geometría de la calzada delante del veh´ıculo. Esta posición relativa varía considerablemente mientras el vehículo circula, por tanto, resulta de gran interés poder estimarla para su aplicación en múltiples aplicaciones basadas en visión por computador, tales como: sistemas avanzados de ayuda a la conducción, vehículos autónomos o robots. Estos continuos cambios en la posición del sistema estéreo se traducen en variaciones en los valores de los parámetros extrínsecos (altura, ángulo de cabeceo y ángulo de alabeo). La validación del método de autocalibración es realizada mediante el empleo de un algoritmo de odometría visual, donde se evalúa la mejora en los resultados que supone conocer en todo momento el valor de los parámetros extrínsecos del sistema estéreo.
Analysis of the Influence of Training Data on Road User Detection
(IEEE, 2018-09-12) Guindel Gómez, Carlos; Martín Gómez, David; Armingol Moreno, José María; Stiller, Christoph; Comunidad de Madrid
In this paper, we discuss the relevance of training data on modern object detectors used on onboard applications. Whereas modern deep learning techniques require large amounts of data, datasets with typical scenarios for autonomous vehicles are scarce and have a reduced number of samples. We conduct a comprehensive set of experiments to understand the effect of using a combination of two relatively small datasets to train an end-to-end object detector, based on the popular Faster R-CNN and enhanced with orientation estimation capabilities. We also test the adequacy of training models using partially available ground-truth labels, as a consequence of combining datasets aimed at different applications. Data augmentation is also introduced into the training pipeline. Results show a significant performance improvement in our exemplary case as a result of the higher variability of the training samples, thus opening a new way to improve the detection performance independently from the detector architecture.
Integration of Dual-Arm Manipulation in a Passivity Based Whole-Body Controller for Torque-Controlled Humanoid Robots
(IEEE, 2020-03-16) García Haro, Juan Miguel; Henze, Bernd; Mesesan, George; Martínez de la Casa Díaz, Santiago; Ott, Christian; Comunidad de Madrid; European Commission; Ministerio de Economía y Competitividad (España)
This work presents an extension of balance control for torque-controlled humanoid robots. Within a non-strict task hierarchy, the controller allows the robot to use the feet end-effectors to balance, while the remaining hand end-effectors can be used to perform Dual-Arm manipulation. The controller generates a passive and compliance behaviour to regulate the location of the centre of mass (CoM), the orientation of the hip and the poses of each end-effector assigned to the task of interaction (in this case bi-manipulation). Then, an appropriate wrench (force and torque) is applied to each of the end-effectors employed for the task to achieve this purpose. Now, in this new controller, the essential requirement focuses on the fact that the desired wrench in the CoM is computed through the sum of the balancing and bi-manipulation wrenches. The bi-manipulation wrenches are obtained through a new dynamic model that allows executing tasks of approaching the grip and manipulation of large objects compliantly. On the other hand, the feedback controller has been maintained but in combination with a bi-manipulation-oriented feedforward control to improve the performance in the object trajectory tracking. This controller is tested in different experiments with the robot TORO.
Visual feature tracking based on PHD filter for vehicle detection
(IEEE, 2014-10-07) García, Fernando; Prioletti, Antonio; Cerri, Pietro; Broggi, Alberto; Escalera Hueso, Arturo de la; Armingol Moreno, José María
Vehicle detection is one of the classical application among the Advance Driver Assistance Systems (ADAS). Applications like emergency braking or adaptive cruise control (ACC) require accurate and reliable vehicle detection. In latest years the improvements in vision detection have lead to the introduction of computer vision to detect vehicles by means of these more economical sensors, with high reliability. In the present paper, a novel algorithm for vehicle detection and tracking based on a probability hypothesis density (PHD) filter is presented. The first detection is based on a fast machine learning algorithm (Adaboost) and Haar-Like features. Later, the tracking is performed, by means features detected within the bounding box provided by the vehicle detection. The features, are tracked by a PHD filter. The results of the features being tracked are combined together in the last step, based on several different methods. Test provided show the performance of the PHD filter in public sequences using the different methods proposed.
Automatic laser and camera extrinsic calibration for data fusion using road plane
(IEEE, 2014-10-07) Rodríguez-Garavito, C. H.; Ponz Vila, Aurelio; García, Fernando; Martín Gómez, David; Escalera Hueso, Arturo de la; Armingol Moreno, José María
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.
Continuous pose estimation for stereo vision based on UV disparity applied to visual odometry in urban environments
(IEEE, 2014) Musleh Lancis, Basam; Martín Gómez, David; Armingol Moreno, José María; Escalera Hueso, Arturo de la
Abstract: :This paper presents an autocalibration method to determine the pose of a stereo vision system based on knowing the geometry of the ground in front of the cameras. This pose changes considerably while the vehicle is driven, therefore it is good to know constantly the pose of the camera for several applications based on computer vision, such as advanced driver assistance systems, autonomous vehicles or robotics. These constant changes of the pose make interesting to be able to detect constantly the variations in its extrinsic parameters (height, pitch, roll). The validation of the autocalibration method is accomplished by a visual odometry implementation. A study of the improvement of the results of the visual odometry estimation taking into account the changes of the camera pose is presented, demonstrating the advantages of the autocalibration method.
SACAT: An instrumented vehicle for driver assistance and safety
(IEEE, 2012-09-05) Aliane, Nourdine; Fernández, Javier; Bemposta Rosende, Sergio; Mata Ortega, Mario; Diez Zaera, Ramiro
The present paper describes the framework and components of an instrumented vehicle for driver assistance and safety. The experimental platform is based on the use of an on-board computer vision system to capture the traffic signs, and on a multiple of electronic components to capture the vehicle state and identify drivers. The hardware architecture is designed with the purpose of making the deployment of functionalities related to driver assistance and road safety easy. The paper covers firstly the description of the hardware architecture, and then describes some of the implemented functionalities such as driver assistance based on traffic signs detection and recognition, traffic violation recorder, and a realization of an emergency call system.
Enhanced obstacle detection based on Data Fusion for ADAS applications
(IEEE, 2013) García, Fernando; Escalera Hueso, Arturo de la; Armingol Moreno, José María
Abstract: Fusion is a common topic nowadays in Advanced Driver Assistance Systems (ADAS). The demanding requirements of safety applications require trustable sensing technologies. Fusion allows to provide trustable detections by combining different sensor devices, fulfilling the requirements of safety applications. High level fusion scheme is presented; able to improve classic ADAS systems by combining different sensing technologies i.e. laser scanner and computer vision. By means of powerful Data Fusion (DF) algorithms, the performance of classic ADAS detection systems is enhanced. Fusion is performed in a decentralized scheme (high level), allowing scalability; hence new sensing technologies can easily be added to increase the trustability and the accuracy of the overall system. Present work focus in the Data Fusion scheme used to combine the information of the sensors at high level. Although for completeness some details of the different detection algorithms (low level) of the different sensors is provided. The proposed work adapts a powerful Data Association technique for Multiple Targets Tracking (MTT): Joint Probabilistic Data Association (JPDA) to improve the trustability of the ADAS detection systems. The final application provides real time detection of road users (pedestrians and vehicles) in real road situations. The tests performed proved the improvement of the use of Data Fusion algorithms. Furthermore, comparison with other classic algorithms such as Global Nearest Neighbors (GNN) proved the performance of the overall architecture.
Part based pedestrian detection based on logic inference
(IEEE, 2013) Olmeda Reino, Daniel; Armingol Moreno, José María; Escalera Hueso, Arturo de la
This paper presents an approach on detection of largely occluded pedestrians. From a pair of synchronized cameras in the Visible Light (VL) and Far Infrared (FIR) spectrum individual detections are combined and final confidence is inferred using a small set of logic rules via a Markov Logic Network. Pedestrians not entirely contained in the image or occluded are detected based on the binary classification on subparts of the detection window. The presented method is applied to a pedestrian classification problem in urban environments. The classifier has been tested in an Intelligent Transportation System (ITS) platform as part of an Advanced Driver Assistance Systems (ADAS).
Context Aided Multilevel Pedestrian Detection
(IEEE, 2013) García, Fernando; Escalera Hueso, Arturo de la; Armingol Moreno, José María
Abstract: The proposed work, depicts a novel algorithm able to provide multiple pedestrian detection, based on the use of classical sensors in modern automotive application and context information. The work takes advantage of the use of Joint Probabilities Data Association (JPDA) and context information to enhance the classic performance of the pedestrian detection algorithms. The combination of the different information sources with powerful tracking algorithms helps to overcome the difficulties of this processes, providing a trustable tool that improves performance of the single sensor detection algorithms. Context in a rich information source, able to improve the fusion process in all levels by the use of a priori knowledge of the application. In the present work multilevel fusion solution is provided for road safety application. Context is used in all the fusion levels, helping to improve the perception of the road environment and the relations among detections. By the fusion of all information sources, accurate and trustable detection is provided and complete situation assessment obtained, with estimation of the danger that involves any detection.
Joint Probabilistic Data Association fusion approach for pedestrian detection
(Ieee - The Institute Of Electrical And Electronics Engineers, Inc, 2013) García, Fernando; Escalera Hueso, Arturo de la; Armingol Moreno, José María
Abstract: Fusion is becoming a classic topic in Intelligent Transport System (ITS) society. The lack of trustworthy sensors requires the combination of several devices to provide reliable detections. In this paper a novel approach, that takes advantage of the Joint Probabilistic Data Association technique (JPDA) for data association, is presented. The approach uses one of the most powerful techniques of Multiple Target Tracking theory and adapts it to fulfill the strong requirements of road safety applications. The different test performed proved that a powerful association technique can enhance the capacity of Advance Driver Assistance Systems. Two main sensors are used for pedestrian detection: laser scanner and computer vision. Furthermore, the approach takes advantage of the availability of other information sources i.e. context information and online information (GPS). The detections are fused using JPDA, enhancing the capacities of classical pedestrian detection systems, mainly based in visual information. The test performed also showed that JPDA improved the results offered by other data association techniques, e.g. Global Nearest Neighbors.
Data Fusion for Overtaking Vehicle Detection Based on Radar and Optical Flow
(IEEE, 2012-06-03) García, Fernando; Cerri, Pietro; Broggi, Alberto; Escalera Hueso, Arturo de la; Armingol Moreno, José María
Trustworthiness is a key point when dealing with vehicle safety applications. In this paper an approach to a real application is presented, able to fulfill the requirements of such demanding applications. Most of commercial sensors available nowadays are usually designed to detect front vehicles but lack the ability to detect overtaking vehicles. The work presented here combines the information provided by two sensors, a Stop&Go radar and a camera. Fusion is done by using the unprocessed information from the radar and computer vision based on optical flow. The basic capabilities of the commercial systems are upgraded giving the possibility to improve the front vehicles detection system, by detecting overtaking vehicles with a high positive rate.
Visual Ego Motion Estimation in Urban Environments based on U-V Disparity
(IEEE The Institute Of Electrical And Electronics Engineers, Inc, 2012-06-03) Musleh Lancis, Basam; Martín Gómez, David; Escalera Hueso, Arturo de la; Armingol Moreno, José María
The movement of the vehicle provides useful information for different applications, such as driver assistant systems or autonomous vehicles. This information can be known by means of a GPS, but there are some areas in urban environ ments where the signal is not available, as tunnels or streets with high buildings. A new method for 2D visual ego motion estimation in urban environments is presented in this paper. This method is based on a stereo-vision system where the feature road points are tracked frame to frame in order to estimate the movement of the vehicle, avoiding outliers from dynamic obstacles. The road profile is used to obtain the world coordinates of the feature points as a unique function of its left image coordinates. For these reasons it is only necessary to search feature points in the lower third of the left images. Moreover, the Kalman filter is used as a solution for filtering problem. That is, in some cases, it is necessary to filter raw data due to noise acquisition of time series. The results of the visual ego motion are compared with raw data from a GPS.
U-V Disparity Analysis in Urban Environments
(Springer, 2011-01-06) Musleh Lancis, Basam; Escalera Hueso, Arturo de la; Armingol Moreno, José María
Traditionally obstacles detection is a great topic in computer vision applied to robotics navigation or advance driver assistance system (ADAS). Although other technologies, such as laser, obtain good results to detect obstacles in different environments, stereo vision has the advantage of providing 3D information, improving the knowledge of the environment. A study of the implementation of the u-v disparity in urban environments is presented in this paper, where several tests have been done in different situations which may be difficult to interpret by using a straightforward analysis of the u-v disparity in order to model the environment.
Estimation and prediction of the vehicle's motion basedon visual odometry and Kalman filter
(Springer, 2012) Musleh, Basam; Martín, David; Escalera Hueso, Arturo de la; Guinea, Domingo Miguel; Garcia-Alegre, María Carmen
The movement of the vehicle is an useful information for different applications, such as driver assistant systems or autonomous vehicles. This information can be known by different methods, for instance, by using a GPS or by means of the visual odometry. However, there are some situations where both methods do not work correctly. For example, there are areas in urban environments where the signal of the GPS is not available, as tunnels or streets with high buildings. On the other hand, the algorithms of computer vision are affected by outdoor environments, and the main source of difficulties is the variation in the ligthing conditions. A method to estimate and predict the movement of the vehicle based on visual odometry and Kalman filter is explained in this paper. The Kalman filter allows both filtering and prediction of vehicle motion, using the results from the visual odometry estimation.

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