<p>  </p> <p>This research is motivated by the potential of autonomous mobile robots (AMRs) in enhancing safety, productivity, and efficiency in the construction industry. The dynamic and complex nature of construction sites presents significant challenges to AMRs, particularly in localization and mapping – a process where AMRs determine their own position in the environment while creating a map of the surrounding area. These capabilities are crucial for autonomous navigation and task execution but are inadequately addressed by existing solutions, which primarily rely on visual Simultaneous Localization and Mapping (SLAM) methods. These methods are often ineffective in construction sites due to their underlying assumption of a static environment, leading to unreliable outcomes. Therefore, there is a pressing need to enhance the applicability of AMRs in construction by addressing the limitations of current localization and mapping methods in addressing the dynamic nature of construction sites, thereby empowering AMRs to function more effectively and fully realize their potential in the construction industry.</p> <p>The overarching goal of this research is to fulfill this critical need by developing a novel visual SLAM framework that is capable of not only detecting and segmenting diverse dynamic objects in construction environments but also effectively interpreting the semantic structure of the environment. Furthermore, it can efficiently integrate these functionalities into a unified system to provide an improved SLAM solution for dynamic, complex, and unstructured environments. The rationale is that such a SLAM system could effectively address the dynamic nature of construction sites, thereby significantly improving the efficiency and accuracy of robot localization and mapping in the construction working environment. </p> <p>Towards this goal, three specific objectives have been formulated. The first objective is to develop a novel methodology for comprehensive dynamic object segmentation that can support visual SLAM within highly variable construction environments. This novel method integrates class-agnostic objectness masks and motion cues into video object segmentation, thereby significantly improving the identification and segmentation of dynamic objects within construction sites. These dynamic objects present a significant challenge to the reliable operation of AMRs and, by accurately identifying and segmenting them, the accuracy and reliability of SLAM-based localization is expected to greatly improve. The key to this innovative approach involves a four-stage method for dynamic object segmentation, including objectness mask generation, motion saliency estimation, fusion of objectness masks and motion saliency, and bi-directional propagation of the fused mask. Experimental results show that the proposed method achieves a highest of 6.4% improvement for dynamic object segmentation than state-of-the-art methods, as well as lowest localization errors when integrated into visual SLAM system over public dataset. </p> <p>The second objective focuses on developing a flexible, cost-effective method for semantic segmentation of construction images of structural elements. This method harnesses the power of image-level labels and Building Information Modeling (BIM) object data to replace the traditional and often labor-intensive pixel-level annotations. The hypothesis for this objective is that by fusing image-level labels with BIM-derived object information, a segmentation that is competitive with pixel-level annotations while drastically reducing the associated cost and labor intensity can be achieved. The research method involves initializing object location, extracting object information, and incorporating location priors. Extensive experiments indicate the proposed method with simple image-level labels achieves competitive results with the full pixel-level supervisions, but completely remove the need for laborious and expensive pixel-level annotations when adapting networks to unseen environments. </p> <p>The third objective aims to create an efficient integration of dynamic object segmentation and semantic interpretation within a unified visual SLAM framework. It is proposed that a more efficient dynamic object segmentation with adaptively selected frames combined with the leveraging of a semantic floorplan from an as-built BIM would speed up the removal of dynamic objects and enhance localization while reducing the frequency of scene segmentation. The technical approach to achieving this objective is through two major modifications to the classic visual SLAM system: adaptive dynamic object segmentation, and semantic-based feature reliability update. Upon the accomplishment of this objective, an efficient framework is developed that seamlessly integrates dynamic object segmentation and semantic interpretation into a visual SLAM framework. Experiments demonstrate the proposed framework achieves competitive performance over the testing scenarios, with processing time almost halved than the counterpart dynamic SLAM algorithms.</p> <p>In conclusion, this research contributes significantly to the adoption of AMRs in construction by tailoring a visual SLAM framework specifically for dynamic construction sites. Through the integration of dynamic object segmentation and semantic interpretation, it enhances localization accuracy, mapping efficiency, and overall SLAM performance. With broader implications of visual SLAM algorithms such as site inspection in dangerous zones, progress monitoring, and material transportation, the study promises to advance AMR capabilities, marking a significant step towards a new era in construction automation.</p>

[pt] Robôs móveis são cada dia mais importantes na sociedade moderna, realizando tarefas consideradas tediosas ou muito repetitivas para humanos, como limpeza ou patrulhamento. A maioria dessas tarefas requer um certo nível de autonomia do robô. Para que o robô seja considerado autônomo, ele precisa de um mapa do ambiente, e de sua posição e orientação nesse mapa. O problema de localização e mapeamento simultâneos (SLAM) é a tarefa de estimar tanto o mapa quanto a posição e orientação simultaneamente, usando somente informações dos sensores, sem ajuda externa. O problema de SLAM visual consiste na tarefa de realizar SLAM usando somente câmeras para o sensoriamento. A maior vantagem de usar câmeras é a possibilidade de resolver problemas de visão computacional que provêm informações de alto nível sobre a cena, como detecção de objetos. Porém a maioria dos sistemas de SLAM visual assume um ambiente estático, o que impõe limitações para a sua aplicabilidade em cenários reais. Esta tese apresenta soluções para o problema de SLAM visual em ambientes dinâmicos e em mudança. Especificamente, a tese propõe um método para ambientes com multidões, junto com um detector de pessoas customizado baseado em aprendizado profundo. Além disso, também é proposto um método de SLAM visual para ambientes altamente dinâmicos contendo objetos em movimento, combinando um rastreador de objetos robusto com um algoritmo de filtragem de pontos. Além disso, esta tese propõe um método de SLAM visual para ambientes em mudança, isto é, em cenas onde os objetos podem mudar de lugar após o robô já os ter mapeado. Todos os métodos propostos são testados com dados públicos e experimentos, e comparados com diversos métodos da literatura, alcançando um bom desempenho em tempo real. / [en] Mobile robots have become increasingly important in modern society, as they can perform tasks that are tedious or too repetitive for humans, such as cleaning and patrolling. Most of these tasks require a certain level of autonomy of the robot. To be fully autonomous and perform navigation, the robot needs a map of the environment and its pose within this map. The Simultaneous Localization and Mapping (SLAM) problem is the task of estimating both map and localization, simultaneously, only using sensor measurements. The visual SLAM problem is the task of performing SLAM only using cameras for sensing. The main advantage of using cameras is the possibility of solving computer vision problems that provide high-level information about the scene, such as object detection. However, most visual SLAM systems assume a static environment, which imposes a limitation on their applicability in real-world scenarios. This thesis presents solutions to the visual SLAM problem in dynamic and changing environments. A custom deep learning-based people detector allows our solution to deal with crowded environments. Also, a combination of a robust object tracker and a filtering algorithm enables our visual SLAM system to perform well in highly dynamic environments containing moving objects. Furthermore, this thesis proposes a visual SLAM method for changing environments, i.e., in scenes where the objects are moved after the robot has already mapped them. All proposed methods are tested in datasets and experiments and compared with several state-of-the-art methods, achieving high accuracy in real time.

This thesis covers the topic of image matching in a visual SLAM or SfM context. These problems are generally based on a vector representation of the keypoints of one image, called a descriptor, which we seek to map to the keypoints of another, using a similarity measure to compare the descriptors. However, it remains difficult to perform this matching successfully, especially for challenging scenes where illumination changes, occlusions, motion, textureless and similar features are present, leading to mis-matched points. In this thesis, we develop a self-supervised contrastive deep learning framework for computing robust descriptors, particularly for these challenging situations.We use the TartanAir dataset built explicitly for this task, and in which these difficult scene cases are present. Our results show that descriptor learning works, improves scores, and that our method is competitive with traditional methods such as ORB. In particular, the invariance built implicitly by training pairs of positive examples through the construction of a trajectory from a sequence of images, as well as the controlled introduction of ambiguous negative examples during training, have a real observable effect on the scores obtained. / Le présent mémoire traite du sujet de mise en correspondance entre deux images dans un contexte de SLAM visuel ou de SfM. Ces problèmes reposent généralement sur une représentation vectorielle de points saillants d’une image, appelée descripteur, et qu’on cherche à mettre en correspondance avec les points saillants d’une autre, en utilisant une mesure de similarité pour comparer les descripteurs. Cependant, il reste difficile de réaliser cette mise en correspondance avec succès, en particulier pour les scènes difficiles où des changements d’illumination, des occultations, des mouvements, des éléments sans texture, et des éléments similaires sont présents, conduisant à des mises en correspondance incorrectes. Nous développons dans ce mémoire une méthode d’apprentissage profond contrastif auto-supervisé pour calculer des descripteurs robustes, particulièrement à ces situations difficiles. Nous utilisons le jeu de données TartanAir construit explicitement pour cette tâche, et dans lequel ces cas de scènes difficiles sont présents. Nos résultats montrent que l’apprentissage de descripteurs fonctionne, améliore les scores, et que notre méthode est compétitive avec les méthodes traditionnelles telles que ORB. En particulier, l’invariance bâtie implicitement en formant des paires d’exemples positifs grâce à la construction d’une trajectoire depuis une séquence d’images, ainsi que l’introduction contrôlée d’exemples négatifs ambigus pendant l’entraînement a un réel effet observable sur les scores obtenus.

Ce mémoire décrit mes travaux de thèse de doctorat menés au sein de l’équipe ComSee (Computers that See) rattachée à l’axe ISPR (Image, Systèmes de Perception et Robotique) de l’Institut Pascal. Celle-ci a été financée par la Région Auvergne et le Fonds Européen de Développement Régional. Les travaux présentés s’inscrivent dans le cadre d’applications de localisation pour la robotique mobile et la Réalité Augmentée. Le framework réalisé au cours de cette thèse est une approche générique pour l’implémentation d’applications de SLAM : Simultaneous Localization And Mapping (algorithme de localisation par rapport à un modèle simultanément reconstruit). L’approche intègre une multitude de contraintes dans les processus de localisation et de reconstruction. Ces contraintes proviennent de données capteurs mais également d’a priori liés au contexte applicatif. Chaque contrainte est utilisée au sein d’un même algorithme d’optimisation afin d’améliorer l’estimation du mouvement ainsi que la précision du modèle reconstruit. Trois problèmes ont été abordés au cours de ce travail. Le premier concerne l’utilisation de contraintes sur le modèle reconstruit pour l’estimation précise d’objets 3D partiellement connus et présents dans l’environnement. La seconde problématique traite de la fusion de données multi-capteurs, donc hétérogènes et asynchrones, en utilisant un unique algorithme d’optimisation. La dernière problématique concerne la génération automatique et efficace d’algorithmes d’optimisation à contraintes multiples. L’objectif est de proposer une solution temps réel 1 aux problèmes de SLAM à contraintes multiples. Une approche générique est utilisée pour concevoir le framework afin de gérer une multitude de configurations liées aux différentes contraintes des problèmes de SLAM. Un intérêt tout particulier a été porté à la faible consommation de ressources (mémoire et CPU) tout en conservant une grande portabilité. De plus, la méta-programmation est utilisée pour générer automatiquement et spécifiquement les parties les plus complexes du code en fonction du problème à résoudre. La bibliothèque d’optimisation LMA qui a été développée au cours de cette thèse est mise à disposition de la communauté en open-source. Des expérimentations sont présentées à la fois sur des données de synthèse et des données réelles. Un comparatif exhaustif met en évidence les performances de la bibliothèque LMA face aux alternatives les plus utilisées de l’état de l’art. De plus, le framework de SLAM est utilisé sur des problèmes impliquant une difficulté et une quantité de contraintes croissantes. Les applications de robotique mobile et de Réalité Augmentée mettent en évidence des performances temps réel et un niveau de précision qui croît avec le nombre de contraintes utilisées. / This report describes my thesis work conducted within the ComSee (Computers That See) team related to the ISPR axis (ImageS, Perception Systems and Robotics) of Institut Pascal. It was financed by the Auvergne Région and the European Fund of Regional Development. The thesis was motivated by localization issues related to Augmented Reality and autonomous navigation. The framework developed during this thesis is a generic approach to implement SLAM algorithms : Simultaneous Localization And Mapping. The proposed approach use multiple constraints in the localization and mapping processes. Those constraints come from sensors data and also from knowledge given by the application context. Each constraint is used into one optimization algorithm in order to improve the estimation of the motion and the accuracy of the map. Three problems have been tackled. The first deals with constraints on the map to accurately estimate the pose of 3D objects partially known in the environment. The second problem is about merging multiple heterogeneous and asynchronous data coming from different sensors using an optimization algorithm. The last problem is to write an efficient and real-time implementation of the SLAM problem using multiple constraints. A generic approach is used to design the framework and to generate different configurations, according to the constraints, of each SLAM problem. A particular interest has been put in the low computational requirement (in term of memory and CPU) while offering a high portability. Moreover, meta-programming techniques have been used to automatically and specifically generate the more complex parts of the code according to the given problem. The optimization library LMA, developed during this thesis, is made available of the community in open-source. Several experiments were done on synthesis and real data. An exhaustive benchmark shows the performances of the LMA library compared to the most used alternatives of the state of the art. Moreover, the SLAM framework is used on different problems with an increasing difficulty and amount of constraints. Augmented Reality and autonomous navigation applications show the good performances and accuracies in multiple constraints context.

To navigate successfully in a previously unexplored environment, a mobile robot must be able to estimate the spatial relationships of the objects of interest accurately. A Simultaneous Localization and Mapping (SLAM) sys- tem employs its sensors to build incrementally a map of its surroundings and to localize itself in the map simultaneously. The aim of this research project is to develop a SLAM system suitable for self propelled household lawnmowers. The proposed bearing-only SLAM system requires only an omnidirec- tional camera and some inexpensive landmarks. The main advantage of an omnidirectional camera is the panoramic view of all the landmarks in the scene. Placing landmarks in a lawn field to define the working domain is much easier and more flexible than installing the perimeter wire required by existing autonomous lawnmowers. The common approach of existing bearing-only SLAM methods relies on a motion model for predicting the robot’s pose and a sensor model for updating the pose. In the motion model, the error on the estimates of object positions is cumulated due mainly to the wheel slippage. Quantifying accu- rately the uncertainty of object positions is a fundamental requirement. In bearing-only SLAM, the Probability Density Function (PDF) of landmark position should be uniform along the observed bearing. Existing methods that approximate the PDF with a Gaussian estimation do not satisfy this uniformity requirement. This thesis introduces both geometric and proba- bilistic methods to address the above problems. The main novel contribu- tions of this thesis are: 1. A bearing-only SLAM method not requiring odometry. The proposed method relies solely on the sensor model (landmark bearings only) without relying on the motion model (odometry). The uncertainty of the estimated landmark positions depends on the vision error only, instead of the combination of both odometry and vision errors. 2. The transformation of the spatial uncertainty of objects. This thesis introduces a novel method for translating the spatial un- certainty of objects estimated from a moving frame attached to the robot into the global frame attached to the static landmarks in the environment. 3. The characterization of an improved PDF for representing landmark position in bearing-only SLAM. The proposed PDF is expressed in polar coordinates, and the marginal probability on range is constrained to be uniform. Compared to the PDF estimated from a mixture of Gaussians, the PDF developed here has far fewer parameters and can be easily adopted in a probabilistic framework, such as a particle filtering system. The main advantages of our proposed bearing-only SLAM system are its lower production cost and flexibility of use. The proposed system can be adopted in other domestic robots as well, such as vacuum cleaners or robotic toys when terrain is essentially 2D.

Made available in DSpace on 2014-12-17T14:55:06Z (GMT). No. of bitstreams: 1 GutembergSS.pdf: 569123 bytes, checksum: 6f85b5ee47010d2d331986f17689304b (MD5) Previous issue date: 2008-05-30 / This work presents a cooperative navigation systemof a humanoid robot and a wheeled robot using visual information, aiming to navigate the non-instrumented humanoid robot using information obtained from the instrumented wheeled robot. Despite the humanoid not having sensors to its navigation, it can be remotely controlled by infra-red signals. Thus, the wheeled robot can control the humanoid positioning itself behind him and, through visual information, find it and navigate it. The location of the wheeled robot is obtained merging information from odometers and from landmarks detection, using the Extended Kalman Filter. The marks are visually detected, and their features are extracted by image processing. Parameters obtained by image processing are directly used in the Extended Kalman Filter. Thus, while the wheeled robot locates and navigates the humanoid, it also simultaneously calculates its own location and maps the environment (SLAM). The navigation is done through heuristic algorithms based on errors between the actual and desired pose for each robot. The main contribution of this work was the implementation of a cooperative navigation system for two robots based on visual information, which can be extended to other robotic applications, as the ability to control robots without interfering on its hardware, or attaching communication devices / Este trabalho apresenta um sistema de navega??o cooperativa de um rob? human?ide e um rob? com rodas usando informa??o visual, com o objetivo de efetuar a navega??o do rob? human?ide n?o instrumentado utilizando-se das informa??es obtidas do rob? com rodas instrumentado. Apesar do human?ide n?o possuir sensores para sua navega??o, pode ser remotamente controlado por sinal infravermelho. Assim, o rob? com rodas pode controlar o human?ide posicionando-se atr?s dele e, atrav?s de informa??o visual, localiz?-lo e naveg?-lo. A localiza??o do rob? com rodas ? obtida fundindo-se informa??es de odometria e detec??o de marcos utilizando o filtro de Kalman estendido. Os marcos s?o detectados visualmente, e suas caracter?sticas s?o extra?das pelo o processamento da imagem. As informa??es das caracter?sticas da imagem s?o utilizadas diretamente no filtro de Kalman estendido. Assim, enquanto o rob? com rodas localiza e navega o human?ide, realiza tamb?m sua localiza??o e o mapeamento do ambiente simultaneamente (SLAM). A navega??o ? realizada atrav?s de algoritmos heur?sticos baseados nos erros de pose entre a pose dos rob?s e a pose desejada para cada rob?. A principal contribui??o desse trabalho foi a implementa??o de um sistema de navega??o cooperativa entre dois rob?s baseados em informa??o visual, que pode ser estendido para outras aplica??es rob?ticas, dado a possibilidade de se controlar rob?s sem interferir em seu hardware, ou acoplar dispositivos de comunica??o

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.

<p>This thesis proposes SoPC (System on a Programmable<br />Chip) architectures for efficient embedding of vison-based<br />localization and obstacle detection tasks in a navigational<br />pipeline on autonomous mobile robots. The obtained<br />results are equivalent or better in comparison to state-ofthe-<br />art. For localization, an efficient hardware architecture<br />that supports EKF-SLAM&#39;s local map management with<br />seven-dimensional landmarks in real time is developed.<br />For obstacle detection a novel method of object<br />recognition is proposed - detection by identification<br />framework based on single detection window scale. This<br />framework allows adequate algorithmic precision and<br />execution speeds on embedded hardware platforms.</p> / <p>Ova teza bavi se dizajnom SoPC (engl. System on a<br />Programmable Chip) arhitektura i algoritama za efikasnu<br />implementaciju zadataka lokalizacije i detekcije prepreka<br />baziranih na viziji u kontekstu autonomne robotske<br />navigacije. Za lokalizaciju, razvijena je efikasna<br />računarska arhitektura za EKF-SLAM algoritam, koja<br />podržava skladi&scaron;tenje i obradu sedmodimenzionalnih<br />orijentira lokalne mape u realnom vremenu. Za detekciju<br />prepreka je predložena nova metoda prepoznavanja<br />objekata u slici putem prozora detekcije fiksne<br />dimenzije, koja omogućava veću brzinu izvr&scaron;avanja<br />algoritma detekcije na namenskim računarskim<br />platformama.</p>

Un véhicule autonome ou un robot mobile est équipé d'un système de navigation qui doit comporter plusieurs briques fonctionnelles pour traiter de perception, localisation, planification de trajectoires et locomotion. Dès que ce robot ou ce véhicule se déplace dans un environnement humain dense, il exécute en boucle et en temps réel plusieurs fonctions pour envoyer des consignes aux moteurs, pour calculer sa position vis-à-vis d'un repère de référence connu, et pour détecter de potentiels obstacles sur sa trajectoire; du fait de la richesse sémantique des images et du faible coût des caméras, ces fonctions exploitent souvent la vision. Les systèmes embarqués sur ces machines doivent alors intégrer des cartes assez puissantes pour traiter des données visuelles en temps réel. Par ailleurs, les contraintes d'autonomie de ces plateformes imposent de très faibles consommations énergétiques. Cette thèse proposent des architectures de type SOPC (System on Programmable Chip) conçues par une méthodologie de co-design matériel/logiciel pour exécuter de manière efficace les fonctions de localisation et de détection des obstacles à partir de la vision. Les résultats obtenus sont équivalents ou meilleurs que l'état de l'art, concernant la gestion de la carte locale d'amers pour l'odométrie-visuelle par une approche EKF-SLAM, et le rapport vitesse d'exécution sur précision pour ce qui est de la détection d'obstacles par identification dans les images d'objets (piétons, voitures...) sur la base de modèles appris au préalable. / An autonomous mobile platform is endowed with a navigational system which must contain multiple functional bricks: perception, localization, path planning and motion control. As soon as such a robot or vehicle moves in a crowded environment, it continously loops several tasks in real time: sending reference values to motors' actuators, calculating its position in respect to a known reference frame and detection of potential obstacles on its path. Thanks to semantic richness provided by images and to low cost of visual sensors, these tasks often exploit visual cues. Other embedded systems running on these mobile platforms thus demand for an additional integration of high-speed embeddable processing systems capable of treating abundant visual sensorial input in real-time. Moreover, constraints influencing the autonomy of the mobile platform impose low power consumption. This thesis proposes SOPC (System on a Programmable Chip) architectures for efficient embedding of vison-based localization and obstacle detection tasks in a navigational pipeline by making use of the software/hardware co-design methodology. The obtained results are equivalent or better in comparison to state-of-the-art for both EKF-SLAM based visual odometry: regarding the local map size management containing seven-dimensional landmarks and model-based detection-by-identification obstacle detection: algorithmic precision over execution speed metric.

Simultaneous Localization and Mapping (SLAM) is the crucial ability for many autonomous systems to operate in unknown environments. In recent years SLAM development has focused on achieving robustness regarding the challenges the field still faces e.g. dynamic environments. During this thesis work different existing approaches to tackle dynamics with Visual SLAM systems were analyzed by surveying the recent literature within the field. The goal was to define the advantages and drawbacks of the approaches to provide further insight into the field of dynamic SLAM. Furthermore, two methods of different approaches were chosen for experiments and their implementation was documented. Key conclusions from the literature survey and experiments are the following. The exclusion of dynamic objects with regard to camera pose estimation presents promising results. Tracking of dynamic objects provides valuable information when combining SLAM with other tasks e.g. path planning. Moreover, dynamic reconstruction with SLAM offers better scene understanding and analysis of objects’ behavior within an environment. Many solutions rely on pre-processing and heavy hardware requirements due to the nature of the object detection methods. Methods of motion confirmation of objects lack consideration of camera movement, resulting in static objects being excluded from feature extraction. Considerations for future work within the field include accounting for camera movement for motion confirmation and producing available benchmarks that offer evaluation of the SLAM result as well as the dynamic object detection i.e. ground truth for both camera and objects within the scene. / Simultaneous Localization and Mapping (SLAM) är för många autonoma system avgörande för deras förmåga att kunna verka i tidigare outforskade miljöer. Under de senaste åren har SLAM-utvecklingen fokuserat på att uppnå robusthet när det gäller de utmaningar som fältet fortfarande står inför, t.ex. dynamiska miljöer. I detta examensarbete analyserades befintliga metoder för att hantera dynamik med visuella SLAM-system genom att kartlägga den senaste litteraturen inom området. Målet var att definiera för- och nackdelar hos de olika tillvägagångssätten för att bidra med insikter till området dynamisk SLAM. Dessutom valdes två metoder från olika tillvägagångssätt ut för experiment och deras implementering dokumenterades. De viktigaste slutsatserna från litteraturstudien och experimenten är följande. Uteslutningen av dynamiska objekt vid uppskattning av kamerans position ger lovande resultat. Spårning av dynamiska objekt ger värdefull information när SLAM kombineras med andra uppgifter, t.ex. path planning. Dessutom ger dynamisk rekonstruktion med SLAM bättre förståelse om omgivningen och analys av objekts beteende i den kringliggande miljön. Många lösningar är beroende av förbehandling samt ställer höga hårdvarumässiga krav till följd av objektdetekteringsmetodernas natur. Metoder för rörelsebekräftelse av objekt tar inte hänsyn till kamerarörelser, vilket leder till att statiska objekt utesluts från funktionsextraktion. Uppmaningar för framtida studier inom området inkluderar att ta hänsyn till kamerarörelser under rörelsebekräftelse samt att ta ändamålsenliga riktmärken för att möjliggöra tydligare utvärdering av SLAM-resultat såväl som för dynamisk objektdetektion, dvs. referensvärden för både kamerans position såväl som för objekt i scenen.