A proposal to estimate the motion of an underwater vehicle through visual mosaicking

This thesis proposes a solution to the problem of estimating the motion of an Unmanned Underwater Vehicle (UUV). Our approach is based on the integration of the incremental measurements which are provided by a vision system. When the vehicle is close to the underwater terrain, it constructs a visual map (so called "mosaic") of the area where the mission takes place while, at the same time, it localizes itself on this map, following the Concurrent Mapping and Localization strategy. The proposed methodology to achieve this goal is based on a feature-based mosaicking algorithm. A down-looking camera is attached to the underwater vehicle. As the vehicle moves, a sequence of images of the sea-floor is acquired by the camera. For every image of the sequence, a set of characteristic features is detected by means of a corner detector. Then, their correspondences are found in the next image of the sequence. Solving the correspondence problem in an accurate and reliable way is a difficult task in computer vision. We consider different alternatives to solve this problem by introducing a detailed analysis of the textural characteristics of the image. This is done in two phases: first comparing different texture operators individually, and next selecting those that best characterize the point/matching pair and using them together to obtain a more robust characterization. Various alternatives are also studied to merge the information provided by the individual texture operators. Finally, the best approach in terms of robustness and efficiency is proposed. After the correspondences have been solved, for every pair of consecutive images we obtain a list of image features in the first image and their matchings in the next frame. Our aim is now to recover the apparent motion of the camera from these features. Although an accurate texture analysis is devoted to the matching pro-cedure, some false matches (known as outliers) could still appear among the right correspon-dences. For this reason, a robust estimation technique is used to estimate the planar transformation (homography) which explains the dominant motion of the image. Next, this homography is used to warp the processed image to the common mosaic frame, constructing a composite image formed by every frame of the sequence. With the aim of estimating the position of the vehicle as the mosaic is being constructed, the 3D motion of the vehicle can be computed from the measurements obtained by a sonar altimeter and the incremental motion computed from the homography. Unfortunately, as the mosaic increases in size, image local alignment errors increase the inaccuracies associated to the position of the vehicle. Occasionally, the trajectory described by the vehicle may cross over itself. In this situation new information is available, and the system can readjust the position estimates. Our proposal consists not only in localizing the vehicle, but also in readjusting the trajectory described by the vehicle when crossover information is obtained. This is achieved by implementing an Augmented State Kalman Filter (ASKF). Kalman filtering appears as an adequate framework to deal with position estimates and their associated covariances. Finally, some experimental results are shown. A laboratory setup has been used to analyze and evaluate the accuracy of the mosaicking system. This setup enables a quantitative measurement of the accumulated errors of the mosaics created in the lab. Then, the results obtained from real sea trials using the URIS underwater vehicle are shown.

Online Robust 3D Mapping Using Structure from Motion Cues

This paper presents a complete solution for creating accurate 3D textured models from monocular video sequences. The methods are developed within the framework of sequential structure from motion, where a 3D model of the environment is maintained and updated as new visual information becomes available. The camera position is recovered by directly associating the 3D scene model with local image observations. Compared to standard structure from motion techniques, this approach decreases the error accumulation while increasing the robustness to scene occlusions and feature association failures. The obtained 3D information is used to generate high quality, composite visual maps of the scene (mosaics). The visual maps are used to create texture-mapped, realistic views of the scene

Estudi i implementació d’un mètode de reconstrucció 3D basat en SfM i registre de vistes 3D parcials

Aquest projecte es basarà en reconstruir una imatge 3D gran a partir d’una seqüència d’imatges 2D capturades per una càmera. Ens centrem en l’estudi de les bases matemàtiques de la visió per computador així com en diferents mètodes emprats en la reconstrucció 3D d’imatges. Per portar a terme aquest estudi s’utilitza la plataforma de desenvolupament MatLab ja que permet tractar operacions matemàtiques, imatges i matrius de gran tamany amb molta senzillesa, rapidesa i eficiència, per aquesta raó s’usa en moltes recerques sobre aquest tema. El projecte aprofundeix en el tema descrit anteriorment estudiant i implementant un mètode que consisteix en aplicar Structure From Motion (SFM) a pocs frames seguits obtinguts d’una seqüència d’imatges 2D per crear una reconstrucció 3D. Quan s’han creat dues reconstruccions 3D consecutives i fent servir un frame com a mínim en comú entre elles, s’aplica un mètode de registre d’estructures 3D, l’Iterative Closest Point (ICP), per crear una reconstrucció 3D més gran a través d’unir les diferents reconstruccions obtingudes a partir de SfM. El mètode consisteix en anar repetint aquestes operacions fins al final dels frames per poder aconseguir una reconstrucció 3D més gran que les petites imatges que s’aconsegueixen a través de SfM. A la Figura 1 es pot veure un esquema del procés que es segueix. Per avaluar el comportament del mètode, utilitzem un conjunt de seqüències sintètiques i un conjunt de seqüències reals obtingudes a partir d’una càmera. L’objectiu final d’aquest projecte és construir una nova toolbox de MatLab amb tots els mètodes per crear reconstruccions 3D grans per tal que sigui possible tractar amb facilitat aquest problema i seguir-lo desenvolupant en un futur

Recovering Euclidean deformable models from stereo-motion

In this paper we present a novel structure from motion (SfM) approach able to infer 3D deformable models from uncalibrated stereo images. Using a stereo setup dramatically improves the 3D model estimation when the observed 3D shape is mostly deforming without undergoing strong rigid motion. Our approach first calibrates the stereo system automatically and then computes a single metric rigid structure for each frame. Afterwards, these 3D shapes are aligned to a reference view using a RANSAC method in order to compute the mean shape of the object and to select the subset of points on the object which have remained rigid throughout the sequence without deforming. The selected rigid points are then used to compute frame-wise shape registration and to extract the motion parameters robustly from frame to frame. Finally, all this information is used in a global optimization stage with bundle adjustment which allows to refine the frame-wise initial solution and also to recover the non-rigid 3D model. We show results on synthetic and real data that prove the performance of the proposed method even when there is no rigid motion in the original sequence

Efficient 3D scene modeling and mosaicing

El modelat d'escenes és clau en un gran ventall d'aplicacions que van des de la generació mapes fins a la realitat augmentada. Aquesta tesis presenta una solució completa per a la creació de models 3D amb textura. En primer lloc es presenta un mètode de Structure from Motion seqüencial, a on el model 3D de l'entorn s'actualitza a mesura que s'adquireix nova informació visual. La proposta és més precisa i robusta que l'estat de l'art. També s'ha desenvolupat un mètode online, basat en visual bag-of-words, per a la detecció eficient de llaços. Essent una tècnica completament seqüencial i automàtica, permet la reducció de deriva, millorant la navegació i construcció de mapes. Per tal de construir mapes en àrees extenses, es proposa un algorisme de simplificació de models 3D, orientat a aplicacions online. L'eficiència de les propostes s'ha comparat amb altres mètodes utilitzant diversos conjunts de dades submarines i terrestres.

Hand-held 3D-scanner for large surface registration

L'objectiu d'aquesta tesi és l'estudi de les diferents tècniques per alinear vistes tridimensionals. Aquest estudi ens ha permès detectar els principals problemes de les tècniques existents, aprotant una solució novedosa i contribuint resolent algunes de les mancances detectades especialment en l'alineament de vistes a temps real. Per tal d'adquirir les esmentades vistes, s'ha dissenyat un sensor 3D manual que ens permet fer adquisicions tridimensionals amb total llibertat de moviments. Així mateix, s'han estudiat les tècniques de minimització global per tal de reduir els efectes de la propagació de l'error.