Indiana University-Purdue University Indianapolis (IUPUI) / Obstacle detection, avoidance and path finding for autonomous vehicles requires precise information of the vehicle’s system environment for faultless navigation and decision making. As such vision and depth perception sensors have become an integral part of autonomous vehicles in the current research and development of the autonomous industry. The advancements made in vision sensors such as radars, Light Detection And Ranging (LIDAR) sensors and compact high resolution cameras is encouraging, however individual sensors can be prone to error and misinformation due to environmental factors such as scene illumination, object reflectivity and object transparency. The application of sensor fusion in a system, by the utilization of multiple sensors perceiving similar or relatable information over a network, is implemented to provide a more robust and complete system information and minimize the overall perceived error of the system. 3D LIDAR and monocular camera are the most commonly utilized vision sensors for the implementation of sensor fusion. 3D LIDARs boast a high accuracy and resolution for depth capturing for any given environment and have a broad range of applications such as terrain mapping and 3D reconstruction. Despite 3D LIDAR being the superior sensor for depth, the high cost and sensitivity to its environment make it a poor choice for mid-range application such as autonomous rovers, RC cars and robots. 2D LIDARs are more affordable, easily available and have a wider range of applications than 3D LIDARs, making them the more obvious choice for budget projects. The primary objective of this thesis is to implement a smart and robust sensor fusion system using 2D LIDAR and a stereo depth camera to capture depth and color information of an environment. The depth points generated by the LIDAR are fused with the depth map generated by the stereo camera by a Fuzzy system that implements smart fusion and corrects any gaps in the depth information of the stereo camera. The use of Fuzzy system for sensor fusion of 2D LIDAR and stereo camera is a novel approach to the sensor fusion problem and the output of the fuzzy fusion provides higher depth confidence than the individual sensors provide. In this thesis, we will explore the multiple layers of sensor and data fusion that have been applied to the vision system, both on the camera and lidar data individually and in relation to each other. We will go into detail regarding the development and implementation of fuzzy logic based fusion approach, the fuzzification of input data and the method of selection of the fuzzy system for depth specific fusion for the given vision system and how fuzzy logic can be utilized to provide information which is vastly more reliable than the information provided by the camera and LIDAR separately

L'indentation instrumentée est un outil pratique et puissant pour sonder les propriétés mécaniques des matériaux à petite échelle. Cependant, plusieurs erreurs (rugosité de surface, effet de taille d’indentation, la détermination de premier point de contact, etc.) affectent l'essai d’indentation instrumentée (e.g. non reproductibilité de la courbe d’indentation) et conduisent à des imprécisions dans la détermination des propriétés mécaniques des matériaux analysés. Une approche originale est développée dans cette thèse pour la caractérisation précise des propriétés mécaniques des matériaux. Cette approche fondée sur une analyse statistique des courbes d’indentation avec la prise en compte d’erreur dans la détermination du premier point de contact et des effets de la rugosité de surface. L’approche est basée sur une minimisation de la distance (défini comme l'erreur de la profondeur de contact initiale) entre l’ensemble des courbes expérimentales et celles simulées par le modèle de Bernhard de manière à générer une courbe maitresse « unique » représentative du faisceau de courbes expérimentales. La méthode proposée permet de calculer à partir de cette courbe maitresse la macro-dureté et le module d’Young du matériau en tenant compte des erreurs dues à la rugosité de surface et à l'effet de taille en indentation pour les faibles profondeurs de pénétration. La robustesse de la méthode est prouvée par son application à différents groupes d’échantillons, i.e. panels de matériaux à propriétés mécaniques diverses, différents traitements de surface (polissage, sablage) et différentes pointes d’indentation permettant de générer différents états de contraintes locaux. Une liaison quantitative entre la rugosité de surface et l'écart type de l'erreur de la profondeur de contact initiale est établie grâce à une analyse multi- échelle de la rugosité de la surface. La méthode proposée permet de caractériser les propriétés mécaniques des matériaux sans avoir recours à la préparation de surface pouvant potentiellement altérer ses propriétés (e.g. génération de contraintes résiduelles, contamination de surface…). / Instrumented indentation is a practical and powerful tool for probing the mechanical properties of materials at small scales. However, several errors (surface roughness, indentation size effect, determination of first contact point, etc…) affect the instrumented indentation testing (e.g. the low reproducibility of the indentation curves) and lead to inaccuracies in the determination of mechanical properties of materials analyzed. An original approach is developed in this thesis for the accurate characterization of the mechanical properties of materials. This approach is established by a statistical analysis of the indentation curves with taking account of error in determining the first contact point and effects of the surface roughness. This approach is basing on a minimization of the distance (defined as the initial contact depth error) between the experimental indentation curves and the ones simulated with Bernhard’s model in order to generate a “unique” representative curve which enables to represent all the experimental curves. The proposed method permits to calculate the macro-hardness and the Young’s modulus of materials from this representative curve with the consideration of the errors due to the surface roughness and the indentation size effect for shallow penetration. The robustness of the method is proved by its application to different groups of specimens, i.e. different materials with various mechanical properties, different surface preparation methods (polishing, sandblasting) and different indenter tips to generate different states of local stresses. A quantitative link between the surface roughness and the standard deviation of initial contact depth error is established by a multi-scale surface roughness analyzing. The proposed method enables to characterize the mechanical properties of materials without resorting to the surface preparation which may potentially alter its properties (e.g. generation of residual stresses, surface contamination ...).