Portable and Scalable In-vehicle Laboratory Instrumentation for the Design of i-ADAS

According to the WHO (World Health Organization), world-wide deaths from injuries are projected to rise from 5.1 million in 1990 to 8.4 million in 2020, with traffic-related incidents as the major cause for this increase. Intelligent, Advanced Driving Assis­ tance Systems (i-ADAS) provide a number of solutions to these safety challenges. We developed a scalable in-vehicle mobile i-ADAS research platform for the purpose of traffic context analysis and behavioral prediction designed for understanding fun­ damental issues in intelligent vehicles. We outline our approach and describe the in-vehicle instrumentation

Assessment of Camera Pose Estimation Using Geo-Located Images from Simultaneous Localization and Mapping

This research proposes a method for enabling low-cost camera localization using geo-located images generated with factorgraph-based Simultaneous Localization And Mapping (SLAM). The SLAM results are paired with panoramic image data to generate geo-located images, which can be used to locate and orient low-cost cameras. This study determines the efficacy of using a spherical camera and LIDAR sensor to enable localization for a wide range of cameras with low size, weight, power, and cost. This includes determining the accuracy of SLAM when geo-referencing images, along with introducing a promising method for extracting range measurements from monocular images of known features

Reconstrução 3D a partir de imagens stereo

A reconstrução 3D é uma das várias áreas que estão inseridas no campo da Visão Computacional. A reconstrução 3D com recurso a imagens stereo consiste numa técnica ótica. Esta técnica usa como input duas ou mais imagens de uma cena ou de um objeto e permite obter um modelo 3D desta cena ou objeto. Nesta dissertação é apresentada uma metodologia de reconstrução 3D que usa como input um par de imagens stereo, obtidas com recurso a duas webcams. As câmaras usadas para captar estas imagens são calibradas antes de ser iniciada a reconstrução 3D. Com recurso a algoritmos especializados são detetadas as linhas presentes nas imagens captadas. Assim, nesta dissertação apresenta-se também um algoritmo de deteção de linhas baseado na Transformada de Hough. Quando o processo de deteção de linhas termina, são identificadas correspondências entre estas linhas. Os algoritmos criados para este efeito são também apresentados nesta dissertação. Finalmente, é reconstruído o modelo 3D do objeto presente no par de imagens stereo; Abstract: 3D Reconstruction From Stereo Images 3D reconstruction is one of several areas that are included in the Computer Vision field. 3D reconstruction from stereo images is an optical technique. This technique uses two or more images of a scene or object as input and outputs a 3D model of this scece or object. This dissertation introduces a methodology that allows for 3D reconstruction with a pair of stereo images, obtained with two webcams. These cameras are calibrated before the 3D reconstruction starts. The lines present in the pair of stereo images are detected with specialized algorithms. Thus, in this dissertation it is also presented a line detection algorithm based on the Hough Transform. Once the line detection process is completed, the correspondences between the lines detected in the stereo pair are found. The algorithms created to identify these correspondences are also presented in this dissertation. Finally, the 3D model of the object shown in the stereo images is produced

Visual SLAM for Autonomous Navigation of MAVs

This thesis focuses on developing onboard visual simultaneous localization and mapping (SLAM) systems to enable autonomous navigation of micro aerial vehicles (MAVs), which is still a challenging topic considering the limited payload and computational capability that an MAV normally has. In MAV applications, the visual SLAM systems are required to be very efficient, especially when other visual tasks have to be done in parallel. Furthermore, robustness in pose tracking is highly desired in order to enable safe autonomous navigation of an MAV in three-dimensional (3D) space. These challenges motivate the work in this thesis in the following aspects. Firstly, the problem of visual pose estimation for MAVs using an artificial landmark is addressed. An artificial neural network (ANN) is used to robustly recognize this visual marker in cluttered environments. Then a computational projective-geometry method is implemented for relative pose computation based on the retrieved geometry information of the visual marker. The presented vision system can be used not only for pose control of MAVs, but also for providing accurate pose estimates to a monocular visual SLAM system serving as an automatic initialization module for both indoor and outdoor environments. Secondly, autonomous landing on an arbitrarily textured landing site during autonomous navigation of an MAV is achieved. By integrating an efficient local-feature-based object detection algorithm within a monocular visual SLAM system, the MAV is able to search for the landing site autonomously along a predefined path, and land on it once it has been found. Thus, the proposed monocular visual solution enables autonomous navigation of an MAV in parallel with landing site detection. This solution relaxes the assumption made in conventional vision-guided landing systems, which is that the landing site should be located inside the field of view (FOV) of the vision system before initiating the landing task. The third problem that is addressed in this thesis is multi-camera visual SLAM for robust pose tracking of MAVs. Due to the limited FOV of a single camera, pose tracking using monocular visual SLAM may easily fail when the MAV navigates in unknown environments. Previous work addresses this problem mainly by fusing information from other sensors, like an inertial measurement unit (IMU), to achieve robustness of the whole system, which does not improve the robustness of visual SLAM itself. This thesis investigates solutions for improving the pose tracking robustness of a visual SLAM system by utilizing multiple cameras. A mathematical analysis of how measurements from multiple cameras should be integrated in the optimization of visual SLAM is provided. The resulting theory allows those measurements to be used for both robust pose tracking and map updating of the visual SLAM system. Furthermore, such a multi-camera visual SLAM system is modified to be a robust constant-time visual odometry. By integrating this visual odometry with an efficient back-end which consists of loop-closure detection and pose-graph optimization processes, a near-constant time multi-camera visual SLAM system is achieved for autonomous navigation of MAVs in large-scale environments.Diese Arbeit konzentriert sich auf die Entwicklung von integrierten Systemen zur gleichzeitigen Lokalisierung und Kartierung (Simultaneous Localization and Mapping, SLAM) mit Hilfe visueller Sensoren, um die autonome Navigation von kleinen Luftfahrzeugen (Micro Aerial Vehicles, MAVs) zu ermöglichen. Dies ist noch immer ein anspruchsvolles Thema angesichts der meist begrenzten Nutzlast und Rechenleistung eines MAVs. Die dafür eingesetzten visuellen SLAM Systeme müssen sehr effizient zu sein, vor allem wenn parallel noch andere visuelle Aufgaben durchgeführt werden sollen. Darüber hinaus ist eine robuste Positionsschätzung sehr wichtig, um die sichere autonome Navigation des MAVs im dreidimensionalen (3D) Raum zu ermöglichen. Diese Herausforderungen motivieren die vorliegende Arbeit gemäß den folgenden Gesichtspunkten: Zuerst wird das Problem bearbeitet, die Pose eines MAVs mit Hilfe einer künstlichen Markierung visuell zu schätzen. Ein künstliches neuronales Netz wird verwendet, um diese visuelle Markierung auch in anspruchsvollen Umgebungen zuverlässig zu erkennen. Anschließend wird ein Verfahren aus der projektiven Geometrie eingesetzt, um die relative Pose basierend auf der gemessenen Geometrie der visuellen Markierung zu ermitteln. Das vorgestellte Bildverarbeitungssystem kann nicht nur zur Regelung der Pose des MAVs verwendet werden, sondern auch genaue Posenschätzungen zur automatischen Initialisierung eines monokularen visuellen SLAM-Systems im Innen- und Außenbereich liefern. Anschließend wird die autonome Landung eines MAVs auf einem beliebig texturierten Landeplatz während autonomer Navigation erreicht. Durch die Integration eines effizienten Objekterkennungsalgorithmus, basierend auf lokalen Bildmerkmalen in einem monokularen visuellen SLAM-System, ist das MAV in der Lage den Landeplatz autonom entlang einer vorgegebenen Strecke zu suchen, und auf ihm zu landen sobald er gefunden wurde. Die vorgestellte Lösung ermöglicht somit die autonome Navigation eines MAVs bei paralleler Landeplatzerkennung. Diese Lösung lockert die gängige Annahme in herkömmlichen Systemen zum kamerageführten Landen, dass der Landeplatz vor Beginn der Landung innerhalb des Sichtfelds des Bildverarbeitungssystems liegen muss. Das dritte in dieser Arbeit bearbeitete Problem ist visuelles SLAM mit mehreren Kameras zur robusten Posenschätzung für MAVs. Aufgrund des begrenzten Sichtfelds von einer einzigen Kamera kann die Posenschätzung von monokularem visuellem SLAM leicht fehlschlagen, wenn sich das MAV in einer unbekannten Umgebung bewegt. Frühere Arbeiten versutchen dieses Problem hauptsächlich durch die Fusionierung von Informationen anderer Sensoren, z.B. eines Inertialsensors (Inertial Measurement Unit, IMU) zu lösen um eine höhere Robustheit des Gesamtsystems zu erreichen, was die Robustheit des visuellen SLAM-Systems selbst nicht verbessert. Die vorliegende Arbeit untersucht Lösungen zur Verbesserung der Robustheit der Posenschätzung eines visuellen SLAM-Systems durch die Verwendung mehrerer Kameras. Wie Messungen von mehreren Kameras in die Optimierung für visuelles SLAM integriert werden können wird mathematisch analysiert. Die daraus resultierende Theorie erlaubt die Nutzung dieser Messungen sowohl zur robusten Posenschätzung als auch zur Aktualisierung der visuellen Karte. Ferner wird ein solches visuelles SLAM-System mit mehreren Kameras modifiziert, um in konstanter Laufzeit robuste visuelle Odometrie zu berechnen. Die Integration dieser visuellen Odometrie mit einem effizienten Back-End zur Erkennung von geschlossener Schleifen und der Optimierung des Posengraphen ermöglicht ein visuelles SLAM-System mit mehreren Kameras und fast konstanter Laufzeit zur autonomen Navigation von MAVs in großen Umgebungen