Multi-camera simultaneous localization and mapping

In this thesis, we study two aspects of simultaneous localization and mapping (SLAM) for multi-camera systems: minimal solution methods for the scaled motion of non-overlapping and partially overlapping two camera systems and enabling online, real-time mapping of large areas using the parallelism inherent in the visual simultaneous localization and mapping (VSLAM) problem. We present the only existing minimal solution method for six degree of freedom structure and motion estimation using a non-overlapping, rigid two camera system with known intrinsic and extrinsic calibration. One example application of our method is the three-dimensional reconstruction of urban scenes from video. Because our method does not require the cameras' fields-of-view to overlap, we are able to maximize coverage of the scene and avoid processing redundant, overlapping imagery. Additionally, we developed a minimal solution method for partially overlapping stereo camera systems to overcome degeneracies inherent to non-overlapping two-camera systems but still have a wide total field of view. The method takes two stereo images as its input. It uses one feature visible in all four views and three features visible across two temporal view pairs to constrain the system camera's motion. We show in synthetic experiments that our method creates rotation and translation estimates that are more accurate than the perspective three-point method as the overlap in the stereo camera's fields-of-view is reduced. A final part of this thesis is the development of an online, real-time visual SLAM system that achieves real-time speed by exploiting the parallelism inherent in the VSLAM problem. We show that feature tracking, relative pose estimation, and global mapping operations such as loop detection and loop correction can be effectively parallelized. Additionally, we demonstrate that a combination of short baseline, differentially tracked corner features, which can be tracked at high frame rates and wide baseline matchable but slower to compute features such as the scale-invariant feature transform can facilitate high speed visual odometry and at the same time support location recognition for loop detection and global geometric error correction

Análisis de entornos urbanos de tráfico y estimación del movimiento del vehículo para el desarrollo de sistemas avanzados de ayuda a la conducción

Los entornos urbanos de tráfico representan, por su alta complejidad, un desafío para los sistemas inteligentes de transporte, debido a la gran variedad de situaciones y elementos diferentes que pueden acontecer en estos entornos y que deben ser manejadas por estos sistemas. A este respecto, las soluciones presentadas hasta el momento son variadas en lo concerniente a sensores y métodos, obteniendo estos trabajos resultados muy dispares de precisión, complejidad, coste o carga computacional. El trabajo presentado en esta disertación, desarrolla un conjunto de algoritmos y métodos para dar soporte a la implementación de una gran variedad de sistemas avanzados de ayuda a la conducción o navegación autónoma en estos entornos. El sistema descrito se basa en el análisis del entorno del vehículo y la estimación del movimiento del mismo mediante el empleo de un sistema de visión estereoscópica, donde se ha prestado una especial atención, a la hora de definir las características del desarrollo, a posibilitar su implementación en tiempo real. Se ha hecho hincapié tanto en la justificación matemática de los algoritmos presentados, como en la evaluación del efecto de la variación de los valores de configuración de funcionamiento del sistema, evaluándose a su vez los resultados del mismo mediante el empleo de bases de datos de acceso público, analizándose cerca de 11.000 imágenes a lo largo de 9 km de recorrido en entornos urbanos.Traffic urban environments represent, because of its complexity, a challenge for Intelligent Transport Systems due to the great variety of situations and different elements that can happen in these environments and that must be faced by these systems. In connection with this, so far there are a variety of solutions as regards sensors and methods, so the results of precision, complexity, cost or computational load obtained by these works are different. The work presented in this dissertation develops a set of algorithms and methods in order to give support to the implementation of a great variety of advanced driver assistance systems or autonomous navigation in these environments. The system described is based on the analysis of the vehicle environment and its motion estimation by using a stereoscopic vision system which focuses, when defining the characteristics of the development, on enable its implementation in real time. It has been emphasized both the mathematical justification of the algorithms presented and the evaluation of the effect of varying the settings of the system, evaluating its results by using database access public, analyzing around 11,000 images along 9 km in urban environments.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Matilde Santos Peñas.- Secretario: María Araceli Sanchis de Miguel.- Vocal: Felipe Jiménez Alons

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

ICP stereo visual odometry for wheeled vehicles based on a 1DOF motion prior

In this paper, we propose a novel, efficient stereo visual-odometry algorithm for ground vehicles moving in outdoor environments. To avoid the drawbacks of computationally-expensive outlier-removal steps based on random-sample schemes, we use a single-degree-of-freedom kinematic model of the vehicle to initialize an Iterative Closest Point (ICP) algorithm that is utilized to select high-quality inliers. The motion is then computed incrementally from the inliers using a standard linear 3D-to-2D pose-estimation method without any additional batch optimization. The performance of the approach is evaluated against state-of-the-art methods on both synthetic data and publicly-available datasets (e.g., KITTI and Devon Island) collected over several kilometers in both urban environments and challenging off-road terrains. Experiments show that the our algorithm outperforms state-of-the-art approaches in accuracy, runtime, and ease of implementation

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field