DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes

The assumption of scene rigidity is typical in SLAM algorithms. Such a strong assumption limits the use of most visual SLAM systems in populated real-world environments, which are the target of several relevant applications like service robotics or autonomous vehicles. In this paper we present DynaSLAM, a visual SLAM system that, building over ORB-SLAM2 [1], adds the capabilities of dynamic object detection and background inpainting. DynaSLAM is robust in dynamic scenarios for monocular, stereo and RGB-D configurations. We are capable of detecting the moving objects either by multi-view geometry, deep learning or both. Having a static map of the scene allows inpainting the frame background that has been occluded by such dynamic objects. We evaluate our system in public monocular, stereo and RGB-D datasets. We study the impact of several accuracy/speed trade-offs to assess the limits of the proposed methodology. DynaSLAM outperforms the accuracy of standard visual SLAM baselines in highly dynamic scenarios. And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments.Comment: This work has been accepted at IEEE Robotics and Automation Letters, and will be presented at the IEEE Conference on Intelligent Robots and Systems 201

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems

Vehicle and Traffic Safety

The book is devoted to contemporary issues regarding the safety of motor vehicles and road traffic. It presents the achievements of scientists, specialists, and industry representatives in the following selected areas of road transport safety and automotive engineering: active and passive vehicle safety, vehicle dynamics and stability, testing of vehicles (and their assemblies), including electric cars as well as autonomous vehicles. Selected issues from the area of accident analysis and reconstruction are discussed. The impact on road safety of aspects such as traffic control systems, road infrastructure, and human factors is also considered

Object recognition for an autonomous wheelchair equipped with a RGB-D camera

This thesis has been carried out within a project at AR Lab (Autonomous Robot Laboratory) and IAS-Lab (Intelligent Autonomous Systems Lab) of Shanghai Jiao Tong University and University of Padua respectively. The project aims to create a system to recognize and localize multiple object classes for an autonomous wheelchair called JiaoLong, and in general for a mobile robot. The thesis had as main objective the creation of an object recognition and localization system in an indoor environment through a RGB-D sensor. The approach we followed is based on the recognition of the object by using 2D algorithm and 3D information to identify location and size of it. This will help to obtained robust performance for the recognition step and accurate estimation for the localization, thus changing the behavior of the robot in accordance with the class and the location of the object in the room. This thesis is mainly based on two aspects: • the creation of a 2D module to recognize and detect the object in a RGB image; • the creation of a 3D module to filter point cloud and estimate pose and size of the object. In this thesis we used the Bag of Features algorithm to perform the recognition of objects and a variation of the Constellation Method algorithm for the detection; 3D data are computed with several filtering algorithms which lead to a 3D analysis of the object, then are used the intrinsic information of point cloud for the pose and size estimation. We will also analyze the performance of the algorithm and propose some improvements aimed to increase the overall performance of the system besides research directions that this project could lea

Visual slam in dynamic environments

El problema de localización y construcción visual simultánea de mapas (visual SLAM por sus siglas en inglés Simultaneous Localization and Mapping) consiste en localizar una cámara en un mapa que se construye de manera online. Esta tecnología permite la localización de robots en entornos desconocidos y la creación de un mapa de la zona con los sensores que lleva incorporados, es decir, sin contar con ninguna infraestructura externa. A diferencia de los enfoques de odometría en los cuales el movimiento incremental es integrado en el tiempo, un mapa permite que el sensor se localice continuamente en el mismo entorno sin acumular deriva.Asumir que la escena observada es estática es común en los algoritmos de SLAM visual. Aunque la suposición estática es válida para algunas aplicaciones, limita su utilidad en escenas concurridas del mundo real para la conducción autónoma, los robots de servicio o realidad aumentada y virtual entre otros. La detección y el estudio de objetos dinámicos es un requisito para estimar con precisión la posición del sensor y construir mapas estables, útiles para aplicaciones robóticas que operan a largo plazo.Las contribuciones principales de esta tesis son tres: 1. Somos capaces de detectar objetos dinámicos con la ayuda del uso de la segmentación semántica proveniente del aprendizaje profundo y el uso de enfoques de geometría multivisión. Esto nos permite lograr una precisión en la estimación de la trayectoria de la cámara en escenas altamente dinámicas comparable a la que se logra en entornos estáticos, así como construir mapas en 3D que contienen sólo la estructura del entorno estático y estable. 2. Logramos alucinar con imágenes realistas la estructura estática de la escena detrás de los objetos dinámicos. Esto nos permite ofrecer mapas completos con una representación plausible de la escena sin discontinuidades o vacíos ocasionados por las oclusiones de los objetos dinámicos. El reconocimiento visual de lugares también se ve impulsado por estos avances en el procesamiento de imágenes. 3. Desarrollamos un marco conjunto tanto para resolver el problema de SLAM como el seguimiento de múltiples objetos con el fin de obtener un mapa espacio-temporal con información de la trayectoria del sensor y de los alrededores. La comprensión de los objetos dinámicos circundantes es de crucial importancia para los nuevos requisitos de las aplicaciones emergentes de realidad aumentada/virtual o de la navegación autónoma. Estas tres contribuciones hacen avanzar el estado del arte en SLAM visual. Como un producto secundario de nuestra investigación y para el beneficio de la comunidad científica, hemos liberado el código que implementa las soluciones propuestas.<br /

Multisensor Data Fusion Strategies for Advanced Driver Assistance Systems

Multisensor data fusion and integration is a rapidly evolving research area that requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. Multisensor data fusion refers to the synergistic combination of sensory data from multiple sensors and related information to provide more reliable and accurate information than could be achieved using a single, independent sensor (Luo et al., 2007). Actually Multisensor data fusion is a multilevel, multifaceted process dealing with automatic detection, association, correlation, estimation, and combination of data from single and multiple information sources. The results of data fusion process help users make decisions in complicated scenarios. Integration of multiple sensor data was originally needed for military applications in ocean surveillance, air-to air and surface-to-air defence, or battlefield intelligence. More recently, multisensor data fusion has also included the nonmilitary fields of remote environmental sensing, medical diagnosis, automated monitoring of equipment, robotics, and automotive systems (Macci et al., 2008). The potential advantages of multisensor fusion and integration are redundancy, complementarity, timeliness, and cost of the information. The integration or fusion of redundant information can reduce overall uncertainty and thus serve to increase the accuracy with which the features are perceived by the system. Multiple sensors providing redundant information can also serve to increase reliability in the case of sensor error or failure. Complementary information from multiple sensors allows features in the environment to be perceived that are impossible to perceive using just the information from each individual sensor operating separately. (Luo et al., 2007) Besides, driving as one of our daily activities is a complex task involving a great amount of interaction between driver and vehicle. Drivers regularly share their attention among operating the vehicle, monitoring traffic and nearby obstacles, and performing secondary tasks such as conversing, adjusting comfort settings (e.g. temperature, radio.) The complexity of the task and uncertainty of the driving environment make driving a very dangerous task, as according to a study in the European member states, there are more than 1,200,000 traffic accidents a year with over 40,000 fatalities. This fact points up the growing demand for automotive safety systems, which aim for a significant contribution to the overall road safety (Tatschke et al., 2006). Therefore, recently, there are an increased number of research activities focusing on the Driver Assistance System (DAS) development in order O pe n A cc es s D at ab as e w w w .in te ch w eb .o r

Trajectory determination and analysis in sports by satellite and inertial navigation

This research presents methods for performance analysis in sports through the integration of Global Positioning System (GPS) measurements with Inertial Navigation System (INS). The described approach focuses on strapdown inertial navigation using Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMU). A simple inertial error model is proposed and its relevance is proven by comparison to reference data. The concept is then extended to a setup employing several MEMS-IMUs in parallel. The performance of the system is validated with experiments in skiing and motorcycling. The position accuracy achieved with the integrated system varies from decimeter level with dual-frequency differential GPS (DGPS) to 0.7 m for low-cost, single-frequency DGPS. Unlike the position, the velocity accuracy (0.2 m/s) and orientation accuracy (1 – 2 deg) are almost insensitive to the choice of the receiver hardware. The orientation performance, however, is improved by 30 – 50% when integrating four MEMS-IMUs in skew-redundant configuration. Later part of this research introduces a methodology for trajectory comparison. It is shown that trajectories based on dual-frequency GPS positions can be directly modeled and compared using cubic spline smoothing, while those derived from single-frequency DGPS require additional filtering and matching

Development of a Cost-Efficient Multi-Target Classification System Based on FMCW Radar for Security Gate Monitoring

Radar systems have a long history. Like many other great inventions, the origin of radar systems lies in warfare. Only in the last decade, radar systems have found widespread civil use in industrial measurement scenarios and automotive safety applications. Due to their resilience against harsh environments, they are used instead of or in addition to optical or ultrasonic systems. Radar sensors hold excellent capabilities to estimate distance and motion accurately, penetrate non-metallic objects, and remain unaffected by weather conditions. These capabilities make these devices extremely flexible in their applications. Electromagnetic waves centered at frequencies around 24 GHz offer high precision target measurements, compact antenna, and circuitry design, and lower atmospheric absorption than higher frequency-based systems. This thesis studies non-cooperative automatic radar multi-target detection and classification. A prototype of a radar system with a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets passing through a road gate is presented. It allows identifying different types of targets, i.e., pedestrians, motorcycles, cars, and trucks. The developed system is based on a low-cost 24 GHz off-the-shelf FMCW radar, combined with an embedded Raspberry Pi PC for data acquisition and transmission to a remote processing PC, which takes care of detection and classification. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by the radar. The developed method is based on an ad-hoc processing chain to accomplish the automatic target recognition task, which consists of blocks performing clutter and leakage removal with a frame subtraction technique, clustering with a DBSCAN approach, tracking algorithm based on the \u3b1-\u3b2 filter to follow the targets during traversal, features extraction, and finally classification of targets with a classification scheme based on support vector machines. The approach is validated in real experimental scenarios, showing its capabilities incorrectly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks). The approach has been validated with experimental data acquired in different scenarios, showing good identification capabilities