Les véhicules autonomes se mouvant dans un environnement quelconque peuvent être confrontés à la présence de nombreux objets mobiles, dont la localisation et la trajectoire sont indépendantes. Cette problématique est particulièrement présente dans la thématique du transport en milieu urbain, mais sa portée est plus générale. On présente dans cette thèse une solution de perception d'un environnement dynamique, à partir d'une paire de caméra, qui vise à fournir en temps réel une cartographie en trois dimensions de l'environnement courant, ainsi que la vitesse indépendante des points suivis. Cette solution propose par ailleurs des algorithmes de détection, segmentation et suivi des objets mobiles, et fourni donc une liste des objets mobiles de l'environnement, dont les positions, dimensions et vitesses sont connues.

Made available in DSpace on 2014-12-17T14:54:48Z (GMT). No. of bitstreams: 1 DiogoPFP_Tese.pdf: 4402228 bytes, checksum: 17eacb6b5f1731f405518c976d32f701 (MD5) Previous issue date: 2006-05-19 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The objective of this thesis is proposes a method for a mobile robot to build a hybrid map of an indoor, semi-structured environment. The topological part of this map deals with spatial relationships among rooms and corridors. It is a topology-based map, where the edges of the graph are rooms or corridors, and each link between two distinct edges represents a door. The metric part of the map consists in a set of parameters. These parameters describe a geometric figure which adapts to the free space of the local environment. This figure is calculated by a set of points which sample the boundaries of the local free space. These points are obtained with range sensors and with knowledge about the robot s pose. A method based on generalized Hough transform is applied to this set of points in order to obtain the geomtric figure. The building of the hybrid map is an incremental procedure. It is accomplished while the robot explores the environment. Each room is associated with a metric local map and, consequently, with an edge of the topo-logical map. During the mapping procedure, the robot may use recent metric information of the environment to improve its global or relative pose / Esta tese tem o objetivo de propor uma metodologia para constru??o de um mapa h?brido de um ambiente interno. A parte topol?gica da representa??o trata das rela??es de conectividade existentes entre as salas e corredores, sendo assim um grafo que representa a topologia do ambiente global. A parte m?trica consiste em armazenar um conjunto de par?metros que descreve uma figura geom?trica plana que melhor se ajusta ao espa?o livre local. Esta figura ? calculada atrav?s do conhecimento de pontos, ou amostras, dos limites do espa?o livre. Estes pontos s?o obtidos com sensores de dist?ncia e a informa??o ? complementada com a estimativa da pose do rob?. Uma vez que estes pontos est?o determinados, o rob? ent?o aplica uma ferramenta baseada na transformada generalizada de Hough para obter a figura em quest?o. O processo de constru??o do mapa ? incremental e totalmente realizado enquanto o rob? explora o ambiente. Cada sala ? representada por este mapa local e cada n? do grafo que representa a topologia do ambiente est? associado a este mapa. Durante o mapeamento o rob? pode utilizar as informa??es rec?m-adquiridas do ambiente para obter uma melhor estimativa de sua pose global ou relativa a uma sala ou corredor

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Visual simultaneous localization and mapping (SLAM) as field has been researched for ten years, but with recent advances in mobile performance visual SLAM is entering the consumer market in a completely new way. A visual SLAM system will however be sensitive to non cautious use that may result in severe motion, occlusion or poor surroundings in terms of visual features that will cause the system to temporarily fail. The procedure of recovering from such a fail is called relocalization. Together with two similar problems localization, to find your position in an existing SLAM session, and loop closing, the online reparation and perfection of the map in an active SLAM session, these can be grouped as visual location recognition (VLR). This thesis presents novel results by combining the scalability of FabMap and the precision of 13th Lab's tracking yielding high-precision VLR, +/- 10 cm, while maintaining above 99 % precision and 60 % recall for sessions containing thousands of images. Everything functional purely on a normal mobile phone. The applications of VLR are many. Indoors, where GPS is not functioning, VLR can still provide positional information and navigate you through big complexes like airports and museums. Outdoors, VLR can improve the precision of GPS tenfold yielding a new level of navigational experience. Virtual and augmented reality applications are other areas that benefit from improved positioning and localization.

The past decade has seen great progress in research on large scale mapping and perception in static environments. Real world perception requires handling uncertain situations with multiple possible interpretations: e.g. changing appearances, dynamic objects, and varying motion models. These aspects of perception have been largely avoided through the use of heuristics and preprocessing. This thesis is motivated by the challenge of including discrete reasoning directly into the estimation process. We approach the problem by using Conditional Linear Gaussian Networks (CLGNs) as a generalization of least-squares estimation which allows the inclusion of discrete model selection variables. CLGNs are a powerful framework for modeling sparse multi-modal inference problems, but are difficult to solve efficiently. We propose the Iterative Local Model Selection (ILMS) algorithm as a general approximation strategy specifically geared towards the large scale problems encountered in tracking and mapping. Chapter 4 introduces the ILMS algorithm and compares its performance to traditional approximate inference techniques for Switching Linear Dynamical Systems (SLDSs). These evaluations validate the characteristics of the algorithm which make it particularly attractive for applications in robot perception. Chief among these is reliability of convergence, consistent performance, and a reasonable trade off between accuracy and efficiency. In Chapter 5, we show how the data association problem in multi-target tracking can be formulated as an SLDS and effectively solved using ILMS. The SLDS formulation allows the addition of additional discrete variables which model outliers and clutter in the scene. Evaluations on standard pedestrian tracking sequences demonstrates performance competitive with the state of the art. Chapter 6 applies the ILMS algorithm to robust pose graph estimation. A non-linear CLGN is constructed by introducing outlier indicator variables for all loop closures. The standard Gauss-Newton optimization algorithm is modified to use ILMS as an inference algorithm in between linearizations. Experiments demonstrate a large improvement over state-of-the-art robust techniques. The ILMS strategy presented in this thesis is simple and general, but still works surprisingly well. We argue that these properties are encouraging for wider applicability to problems in robot perception.

Dans ce travail, nous proposons une représentation efficace de l’environnement adaptée à la problématique de la navigation autonome. Cette représentation topométrique est constituée d’un graphe de sphères de vision augmentées d’informations de profondeur. Localement la sphère de vision augmentée constitue une représentation égocentrée complète de l’environnement proche. Le graphe de sphères permet de couvrir un environnement de grande taille et d’en assurer la représentation. Les "poses" à 6 degrés de liberté calculées entre sphères sont facilement exploitables par des tâches de navigation en temps réel. Dans cette thèse, les problématiques suivantes ont été considérées : Comment intégrer des informations géométriques et photométriques dans une approche d’odométrie visuelle robuste ; comment déterminer le nombre et le placement des sphères augmentées pour représenter un environnement de façon complète ; comment modéliser les incertitudes pour fusionner les observations dans le but d’augmenter la précision de la représentation ; comment utiliser des cartes de saillances pour augmenter la précision et la stabilité du processus d’odométrie visuelle. / Our aim is concentrated around building ego-centric topometric maps represented as a graph of keyframe nodes which can be efficiently used by autonomous agents. The keyframe nodes which combines a spherical image and a depth map (augmented visual sphere) synthesises information collected in a local area of space by an embedded acquisition system. The representation of the global environment consists of a collection of augmented visual spheres that provide the necessary coverage of an operational area. A "pose" graph that links these spheres together in six degrees of freedom, also defines the domain potentially exploitable for navigation tasks in real time. As part of this research, an approach to map-based representation has been proposed by considering the following issues : how to robustly apply visual odometry by making the most of both photometric and ; geometric information available from our augmented spherical database ; how to determine the quantity and optimal placement of these augmented spheres to cover an environment completely ; how tomodel sensor uncertainties and update the dense infomation of the augmented spheres ; how to compactly represent the information contained in the augmented sphere to ensure robustness, accuracy and stability along an explored trajectory by making use of saliency maps.

When we look at the eld of robotics we nd that exist a lot of types of robots. Some of tham use location navigation and global navigation for their work. This work aims to map options of location navigation and description of basic technique which used. Especially, we will deal with algorithms which work with optical sensors, for example camera, stereocamera or laser which scan medium. Practise section this work is focused on the proposal and implementacion algorithm which working with local navigation for robot's return to the starting position. All this work is connecting with tracked robot which formed in the framework one of project realization at Faculty of information technology.

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Fältstudien gjordes för att utreda om det var möjligt att använda renat, filtrerat avloppsvatten för polymerberedning och i längden även som övrigt processvatten på Ellinge reningsverk. Testerna utfördes i fullskala med både vatten från mellansedimenteringen och slutsedimenteringen. Genom omfattande provtagningar och analysering av resultaten visade det sig vara en fungerande metod utan negativ påverkan på polymerlösningen och slamavvattningen. Det krävs dock mer arbete och ytterligare reningssteg såsom desinficering för att implementera det renade avloppsvattnet på hela vattensystemet. / The field study was done to investigate whether it was possible to use purified, filtered wastewater for polymer preparation and in the long run also as other process water at Ellinge treatment plant. The tests was performed in full scale with both water from the intermediate sedimentation and the final sedimentation. Through extensive sampling and analysis of results, it proved to be a working method without a negative impact on the polymer solution and sludge dewatering. However, more work and addiotional purification steps such as disinfection are required to implement the treated wastewater on the entire water system

The Simultaneous Localisation And Mapping (SLAM) problem is an open fundamental problem in autonomous mobile robotics. One of the latest most researched techniques used to enhance the SLAM methods is instance segmentation. In this thesis, we implement an instance segmentation system using Swin Transformer combined with two of the state of the art methods of instance segmentation namely Cascade Mask RCNN and Mask RCNN. Instance segmentation is a technique that simultaneously solves the problem of object detection and semantic segmentation. We show that depth information enhances the average precision (AP) by approximately 7%. We also show that the Swin Transformer backbone model can work well with depth images. Our results also show that Cascade Mask RCNN outperforms Mask RCNN. However, the results are to be considered due to the small size of the NYU-depth v2 dataset. Most of the instance segmentation researches use the COCO dataset which has a hundred times more images than the NYU-depth v2 dataset but it does not have the depth information of the image.