Extended Kalman Filter Implementation for the Khepera II Mobile Robot

The accurate estimation of robot position and orientation in real-time is one of the fundamental challenges in mobile robotics. The Extended Kalman Filter is a nonlinear real-time recursive time domain filter that combines available sensor data to produce an accurate estimate of state, and has been successfully applied to the localization problem in mobile robotics and aircraft navigation. This report describes an Extended Kalman Filter implementa- tion for the Khepera II mobile robotics platform that seeks to produce accurate localization estimates in real-time using wheel odometry data, IR sensor range data, and compass heading data

Development of Collaborative SLAM Algorithm for Team of Robots

Simultaneous Localization and Mapping (SLAM) is a fundamental problem for building truly automatic robots. Varieties of methods and algorithms have been generated, and applied into mobile robots during the last thirty years. However, each algorithm has its strength and weakness. This thesis studies the most recent published techniques in the field of mobile robot SLAM. Specifically, it focuses on investigating robot path and landmark position estimating errors made by different methods. The Hybrid method, which uses FastSLAM method as front-end and uses EKF-SLAM method as back-end, combines both methods advantages, producing smaller errors on estimating robot pose. The Hybrid method solves the single robot SLAM problems by summing the weighted mean values of each particle in FastSLAM. The contributions of this thesis is it presents an alternate mapping algorithm that extends this single-robot Hybrid SLAM algorithm to a multi-robot SLAM algorithm. In this algorithm, each robot draws map of the environment separately, and robots could transfer their mapping information into a central computer. The central computer could merge the landmark positions from different robots. At last, a revised landmark position as well as its covariance will be calculated. Landmark positions are fused together according to two robots feature information by using Kalman Filters

Auto-localização e mapeamento de ambientes : uma abordagem para robôs simples

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, 2017.Grande parte das pesquisas relacionadas à robótica é focada, principalmente, na mobilidade do robô. Isto ocorre pela necessidade, na maioria das atividades, da navegação e auto-localização no ambiente. Com este objetivo, a técnica de SLAM (Auto- localização e mapeamento simultâneos de ambientes) vem sendo implementada em diversos contextos por toda a comunidade de robótica. Esta pesquisa buscou analisar técnicas renomadas de auto-localização no contexto da robótica mundial, a partir da execução de uma revisão sistemática sobre o tema, selecionando a técnica do Filtro de Partículas para adaptação e implementação no contexto limitado da Robótica Educacional. Durante as etapas de implementação e análise dos resultados, a pesquisa busca documentar de maneira clara e objetiva os procedimentos realizados, garantindo a possibilidade da execução dos procedimentos por interessados no assunto. Além da aplicação no contexto educacional, deve-se ressaltar que esta pesquisa faz referência a utilização de robôs simples no processo de auto-localização, o que abrange sua utilização também em contextos reais, porém com limitações de hardware.This research sought to analyse renowned techniques of auto localization in the context of the current world of robotics, starting from the execution of a sistematic revision about the theme, selecting the Particle Filter to the adaptation and implementation in the limited context of Educational Robotics. During the steps of the analysis of results, the research sought to document clearly and objectively the proceedings, ensuring the possibility of the execution of them by the interested researchers in the theme. Besides the application on the educational context, it must be emphasized that this search makes reference to the utilization of simple robots on the auto localization process, which also includes its utilization on real contexts, with hardware limitations, however

Mapping, planning and exploration with Pose SLAM

This thesis reports research on mapping, path planning, and autonomous exploration. These are classical problems in robotics, typically studied independently, and here we link such problems by framing them within a common SLAM approach, adopting Pose SLAM as the basic state estimation machinery. The main contribution of this thesis is an approach that allows a mobile robot to plan a path using the map it builds with Pose SLAM and to select the appropriate actions to autonomously construct this map. Pose SLAM is the variant of SLAM where only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. In Pose SLAM, observations come in the form of relative-motion measurements between robot poses. With regards to extending the original Pose SLAM formulation, this thesis studies the computation of such measurements when they are obtained with stereo cameras and develops the appropriate noise propagation models for such case. Furthermore, the initial formulation of Pose SLAM assumes poses in SE(2) and in this thesis we extend this formulation to SE(3), parameterizing rotations either with Euler angles and quaternions. We also introduce a loop closure test that exploits the information from the filter using an independent measure of information content between poses. In the application domain, we present a technique to process the 3D volumetric maps obtained with this SLAM methodology, but with laser range scanning as the sensor modality, to derive traversability maps. Aside from these extensions to Pose SLAM, the core contribution of the thesis is an approach for path planning that exploits the modeled uncertainties in Pose SLAM to search for the path in the pose graph with the lowest accumulated robot pose uncertainty, i.e., the path that allows the robot to navigate to a given goal with the least probability of becoming lost. An added advantage of the proposed path planning approach is that since Pose SLAM is agnostic with respect to the sensor modalities used, it can be used in different environments and with different robots, and since the original pose graph may come from a previous mapping session, the paths stored in the map already satisfy constraints not easy modeled in the robot controller, such as the existence of restricted regions, or the right of way along paths. The proposed path planning methodology has been extensively tested both in simulation and with a real outdoor robot. Our path planning approach is adequate for scenarios where a robot is initially guided during map construction, but autonomous during execution. For other scenarios in which more autonomy is required, the robot should be able to explore the environment without any supervision. The second core contribution of this thesis is an autonomous exploration method that complements the aforementioned path planning strategy. The method selects the appropriate actions to drive the robot so as to maximize coverage and at the same time minimize localization and map uncertainties. An occupancy grid is maintained for the sole purpose of guaranteeing coverage. A significant advantage of the method is that since the grid is only computed to hypothesize entropy reduction of candidate map posteriors, it can be computed at a very coarse resolution since it is not used to maintain neither the robot localization estimate, nor the structure of the environment. Our technique evaluates two types of actions: exploratory actions and place revisiting actions. Action decisions are made based on entropy reduction estimates. By maintaining a Pose SLAM estimate at run time, the technique allows to replan trajectories online should significant change in the Pose SLAM estimate be detected. The proposed exploration strategy was tested in a common publicly available dataset comparing favorably against frontier based exploratio

Aplicação de técnicas de fusão sensorial para mapeamento e localização simultâneos para robôs terrestres

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.Um dos problemas que envolvem as soluções para a mobilidade de robôs móveis terrestres é estimar a posição do robô com precisão juntamente com a exploração do ambiente, mapeando-o corretamente (SLAM - Simultaneous Localization and Mapping - Localização e Mapeamento Simultâneo). Embora vários algoritmos tenham sido desenvolvidos nos últimos anos, exigindo uma carga de cálculo computacional cada vez maior dos robôs,, estes estão susceptíveis a um mau desempenho quando os sensores apresentam ruídos, quando há problemas nos atuadores, variáveis não modeladas ou em virtude de algum imprevisto momentâneo no ambiente. A proposta deste trabalho é programar um SLAM para robôs móveis interligando-o a uma combinação de sensores inerciais com sensores de odometria através de uma técnica de fusão de sensores conhecida como filtro de Kalman Estendido, para reduzir a incerteza na estimação da posição e melhorar o desempenho do SLAM. Por consequência, o custo computacional é reduzido. O trabalho foi estruturado iniciando por uma revisão a respeito dos conceitos básicos de sensoriamento, a fim de contextualizar o problema e apresentar as nomenclaturas e termos utilizados. A seguir foram abordadas as técnicas de fusão de dados, as representações do robô e do ambiente, as técnicas de mapeamento e exploração e as diversas técnicas de navegação que podem ser utilizadas, para ambientes conhecidos epara ambientes desconhecidos. Essas informações são importantes para um melhor entendimento do problema, de como representá-lo e de como se pode avaliar os resultados obtidos. Na sequência é apresentado o SLAM, destacando as principais técnicas e em detalhes o Grid Based FastSLAM. É demonstrado através de simulações que quanto maior as incertezas sobre a posição do robô, um número maior de partículas é necessário para manter a qualidade do mapa gerado, e como cada partícula possui um mapa associado a si, o custo computacional é consideravelmente aumentado. Outro aspecto analisado foi o impacto na escolha da covariância associada à transição de estados, propondo a utilização da covariância inerente ao cálculo da fusão de sensores como parâmetro de refinamento no SLAM.Abstract : One of the problems in solutions involving land mobile robots is the estimation of the robot position with precision and at the same time, explore the environment and mapping it correctly (SLAM - Simultaneous Localization and Mapping ). Several algorithms were developed in the last years, demanding large computational resources in robots and even so, these may have a bad performance in cases of sensors having noises, problems in actuators, not modeled variables or when there is something in the environment that wasn't expected. This dissertation proposal is to program a SLAM algorithm for mobile robots and connect it with a sensor data fusion, between odometry and inertial sensors, using the Extended Kalman Filter, achieving a reduction of the position uncertainty and improving the SLAM performance, also reducing the need of computational resources. This work begins with a revision of concepts of robot sensors, needed to understand later algorithms and nomenclatures. In the following items it is described the sensor fusion techniques, the robot localization problem, the map and robot representation alternatives, and the navigation problems for explored and non-explored environments. These information are important for a better understanding of the problem, on how represent it and how to evaluate the obtained results. After this introduction, it's described some SLAM algorithms, featuring in details the Grid Based FastSLAM. It's demonstrated by simulations that as high uncertainty about robot position, as large are the number of particles needed to maintain the generated map quality. This implies in a large computational cost, thus improving the uncertainty with sensor data fusion makes the robot work with less particles. It is also showed that choosing the right covariance in robot transition model is very important and finding a way to connect the covariance of sensor data fusion with SLAM can improve performance even more

Des cartes combinatoires pour la construction automatique de modèles d'environnement par un robot mobile

Ce travail s'inscrit dans la problématique classique de localisation et de cartographie simultanées pour un robot mobile évoluant en milieu intérieur supposé inconnu. Son originalité réside dans la définition d'un modèle de carte très structuré fondé sur un outil algébrique appelé « carte combinatoire », qui combine plusieurs types de représentations géométriques (modèles surfaciques et cartes basées sur des primitives géométriques) et fournit des informations topologiques telles que les liens d'adjacence. Nous détaillons la chaîne algorithmique permettant de construire des cartes en ligne suivant ce modèle, avec un robot équipé d'un télémètre laser à balayage : il s'agit d'adapter les techniques habituelles basées sur le filtrage de Kalman afin de gérer les relations d'adjacence (appariement de chaînes polygonales, définition de points de cassure virtuels, mises à jour géométrique et topologique spécifiques). Des résultats expérimentaux illustrent et valident les divers mécanismes mis en oeuvre. ABSTRACT : This thesis focuses on the well-known Simultaneous Localization And Map-building (SLAM) problem for indoor mobile robots. The novelty of this work lies in the definition of a well-structured map model based on an algebraic tool called « combinatorial map » which combines different kinds of geometric representations (space-based, grid-based as well as feature-based formats) and provides topological information such as adjacency links between map elements. We describe the whole algorithm designed to build maps on line according to this model, using a robot equipped with a laser scanner. Classical techniques relying on Kalman filtering are adapted in order to deal with adjacency relationships (via polyline matching, the use of virtual break-points and specific geometric and topological update operations). Exeprimental results are presented to illustrate and validate the various mecanisms involved in this process