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Abstract

Due to the increase of traffic volume and complexity of new transport systems, new Advanced Driver Assistance Systems (ADAS) are a subject of research of many companies, laboratories and universities. These systems include algorithms with techniques that have been studied during the last decades like Simultaneous Lo- calization and Mapping (SLAM), obstacle detection, stereo vision, etc. Thanks to the advances in electronics, robotics and other domains, new embedded systems are being developed to guarantee the safety of the users of these critical systems. For most of these systems a low power consumption as well as reduced size is required. It creates the constraint of execute the algorithms in embedded devices with limited resources. In most of algorithms, moreover for computer vision ones, a big amount of data must be processed at high frequencies, this amount of data demands strong computing resources. FPGAs satisfy this requirement; its parallel architecture combined with its low power consumption and exibility allows developing and executing some algorithms more efficiently than any other processing platforms. In this thesis different embedded computer vision architectures intended to be used in ADAS using FPGAs are presented such as:  We present the implementation of a distortion correction architecture operating at 100 Hz in two cameras simultaneously. The correction module allows also to rectify two images for implementation of stereo vision.  Obstacle detection algorithms based on Inverse Perspective Mapping (IPM) and classiffication based on Color/Texture attributes are presented. The IPM transform is based in the perspective effect of a scene perceived from two different points of view. Moreover results of the detection algorithms from color/texture attributes applied on a multi-cameras system, are fused in an occupancy grid.  An accelerator to apply homographies on images, is presented; this accelerator can be used for different applications like the generation of Bird's eye view or Side view.  Multispectral vision is studied using both infrared images and color ones. Syn- thetic images are generated from information acquired from visible and infrared sources to provide a visual aid to the driver. Image enhancement specific for infrared images is also implemented and evaluated, based on the Contrast Lim- ited Adaptive Histogram Equalization (CLAHE).  An embedded SLAM algorithm is presented with different hardware acceler- ators (point detection, landmark tracking, active search, correlation, matrix operations). All the algorithms were simulated, implemented and verified using as target FPGAs. The validation was done using development kits. A custom board integrating all the presented algorithms is presented. Virtual components developed in this thesis were used in three different projects: PICASSO (stereo vision), COMMROB (obstacle detection from a multi-cameras system) and SART (multispectral vision).

Résumé

En raison de l'augmentation du volume et de la complexité des systèmes de transport, de nouveaux systèmes avancés d'assistance à la conduite (ADAS) sont étudiés dans de nombreuses entreprises, laboratoires et universités. Ces systèmes comprennent des algorithmes avec des techniques qui ont été étudiés au cours des dernières décennies, comme la localisation et cartographie simultanées (SLAM), détection d'obstacles, la vision stéréoscopique, etc. Grâce aux progrès de l'électronique, de la robotique et de plusieurs autres domaines, de nouveaux systèmes embarqués sont développés pour garantir la sécurité des utilisateurs de ces systèmes critiques. Pour la plupart de ces systèmes, une faible consommation d'énergie ainsi qu’une taille réduite sont nécessaires. Cela crée la contrainte d'exécuter les algorithmes sur les systèmes embarqués avec des ressources limitées. Dans la plupart des algorithmes, en particulier pour la vision par ordinateur, une grande quantité de données doivent être traitées à des fréquences élevées, ce qui exige des ressources informatiques importantes. Un FPGA satisfait cette exigence, son architecture parallèle combinée à sa faible consommation d'énergie et la souplesse pour les programmer permet de développer et d'exécuter des algorithmes plus efficacement que sur d’autres plateformes de traitement. Les composants virtuels développés dans cette thèse ont été utilisés dans trois différents projets: PICASSO (vision stéréoscopique), COMMROB (détection d'obstacles à partir d'une système multicaméra) et SART (Système d’Aide au Roulage tous Temps).

Mots-Clés / Keywords

ADAS; Obstacle detection; IPM; SLAM; Infrared; Multispectral; Multi-camera; Computer vision; Homography; FPGA; Détection d'obstacles; Infrarouge; Multi-spectrale; Multi-caméras; Vision par ordinateur; Homographie;

Abstract

The fundamental matrix can be estimated from point matches. The current gold standard is to bootstrap the eight-point algorithm and two-view projective bundle adjustment. The eight-point algorithm first computes a simple linear least squares solution by minimizing an algebraic cost and then computes the closest rank-deficient matrix. This article proposes a single-step method that solves both steps of the eight-point algorithm. Using recent result from polynomial global optimization, our method finds the rank-deficient matrix that exactly minimizes the algebraic cost. The current gold standard is known to be extremely effective but is nonetheless outperformed by our rank-constrained method boostrapping bundle adjustment. This is here demonstrated on simulated and standard real datasets. With our initialization, bundle adjustment consistently finds a better local minimum (achieves a lower reprojection error) and takes less iterations to converge