Localization in highly dynamic environments using dual-timescale NDT-MCL

Industrial environments are rarely static and often their configuration is continuously changing due to the material transfer flow. This is a major challenge for infrastructure free localization systems. In this paper we address this challenge by introducing a localization approach that uses a dual- timescale approach. The proposed approach - Dual-Timescale Normal Distributions Transform Monte Carlo Localization (DT- NDT-MCL) - is a particle filter based localization method, which simultaneously keeps track of the pose using an apriori known static map and a short-term map. The short-term map is continuously updated and uses Normal Distributions Transform Occupancy maps to maintain the current state of the environment. A key novelty of this approach is that it does not have to select an entire timescale map but rather use the best timescale locally. The approach has real-time performance and is evaluated using three datasets with increasing levels of dynamics. We compare our approach against previously pro- posed NDT-MCL and commonly used SLAM algorithms and show that DT-NDT-MCL outperforms competing algorithms with regards to accuracy in all three test cases.Postprint (author’s final draft

Corrective Gradient Refinement for Mobile Robot Localization

Particle filters for mobile robot localization must balance computational requirements and accuracy of localization. Increasing the number of particles in a particle filter improves accuracy, but also increases the computational requirements. Hence, we investigate a different paradigm to better utilize particles than to increase their numbers. To this end, we introduce the Corrective Gradient Refinement (CGR) algorithm that uses the state space gradients of the observation model to improve accuracy while maintaining low computational requirements. We develop an observation model for mobile robot localization using point cloud sensors (LIDAR and depth cameras) with vector maps. This observation model is then used to analytically compute the state space gradients necessary for CGR. We show experimentally that the resulting complete localization algorithm is more accurate than the Sampling/Importance Resampling Monte Carlo Localization algorithm, while requiring fewer particles

Localization in highly dynamic environments using dual-timescale NDT-MCL

Industrial environments are rarely static and often their configuration is continuously changing due to the material transfer flow. This is a major challenge for infrastructure free localization systems. In this paper we address this challenge by introducing a localization approach that uses a dual- timescale approach. The proposed approach - Dual-Timescale Normal Distributions Transform Monte Carlo Localization (DT- NDT-MCL) - is a particle filter based localization method, which simultaneously keeps track of the pose using an apriori known static map and a short-term map. The short-term map is continuously updated and uses Normal Distributions Transform Occupancy maps to maintain the current state of the environment. A key novelty of this approach is that it does not have to select an entire timescale map but rather use the best timescale locally. The approach has real-time performance and is evaluated using three datasets with increasing levels of dynamics. We compare our approach against previously pro- posed NDT-MCL and commonly used SLAM algorithms and show that DT-NDT-MCL outperforms competing algorithms with regards to accuracy in all three test cases.This work has been supported by the European Commission under contract number FP7-ICT-600877 (SPENCER) and by the Spanish Ministry of Economy and Competitiveness under Project DPI-2011-27510 and the EU Project CargoAnts FP7-605598.Peer Reviewe

Building a grid-semantic map for the navigation of service robots through human–robot interaction

AbstractThis paper presents an interactive approach to the construction of a grid-semantic map for the navigation of service robots in an indoor environment. It is based on the Robot Operating System (ROS) framework and contains four modules, namely Interactive Module, Control Module, Navigation Module and Mapping Module. Three challenging issues have been focused during its development: (i) how human voice and robot visual information could be effectively deployed in the mapping and navigation process; (ii) how semantic names could combine with coordinate data in an online Grid-Semantic map; and (iii) how a localization–evaluate–relocalization method could be used in global localization based on modified maximum particle weight of the particle swarm. A number of experiments are carried out in both simulated and real environments such as corridors and offices to verify its feasibility and performance

Contributions to autonomous robust navigation of mobile robots in industrial applications

151 p.Un aspecto en el que las plataformas móviles actuales se quedan atrás en comparación con el punto que se ha alcanzado ya en la industria es la precisión. La cuarta revolución industrial trajo consigo la implantación de maquinaria en la mayor parte de procesos industriales, y una fortaleza de estos es su repetitividad. Los robots móviles autónomos, que son los que ofrecen una mayor flexibilidad, carecen de esta capacidad, principalmente debido al ruido inherente a las lecturas ofrecidas por los sensores y al dinamismo existente en la mayoría de entornos. Por este motivo, gran parte de este trabajo se centra en cuantificar el error cometido por los principales métodos de mapeado y localización de robots móviles,ofreciendo distintas alternativas para la mejora del posicionamiento.Asimismo, las principales fuentes de información con las que los robots móviles son capaces de realizarlas funciones descritas son los sensores exteroceptivos, los cuales miden el entorno y no tanto el estado del propio robot. Por esta misma razón, algunos métodos son muy dependientes del escenario en el que se han desarrollado, y no obtienen los mismos resultados cuando este varía. La mayoría de plataformas móviles generan un mapa que representa el entorno que les rodea, y fundamentan en este muchos de sus cálculos para realizar acciones como navegar. Dicha generación es un proceso que requiere de intervención humana en la mayoría de casos y que tiene una gran repercusión en el posterior funcionamiento del robot. En la última parte del presente trabajo, se propone un método que pretende optimizar este paso para así generar un modelo más rico del entorno sin requerir de tiempo adicional para ello

Localização e mapeamento eficiente para robótica : algoritmos e ferramentas

Doutoramento conjunto em InformáticaUm dos problemas fundamentais em robótica é a capacidade de estimar a pose de um robô móvel relativamente ao seu ambiente. Este problema é conhecido como localização robótica e a sua exatidão e eficiência têm um impacto direto em todos os sistemas que dependem da localização. Nesta tese, abordamos o problema da localização propondo um algoritmo baseado em scan matching com otimização robusta de mínimos quadrados não lineares em manifold com a utilização de um campo de verosimilhança contínuo como modelo de perceção. Esta solução oferece uma melhoria percetível na eficiência computacional sem perda de exatidão. Associado à localização está o problema de criar uma representação geométrica (ou mapa) do meio ambiente recorrendo às medidas disponíveis, um problema conhecido como mapeamento. No mapeamento a representação geométrica mais popular é a grelha volumétrica que discretiza o espaço em volumes cúbicos de igual tamanho. A implementação direta de uma grelha volumétrica oferece acesso direto e rápido aos dados mas requer uma quantidade substancial de memória. Portanto, propõe-se uma estrutura de dados híbrida, com divisão esparsa do espaço combinada com uma subdivisão densa do espaço que oferece tempos de acesso eficientes com alocações de memória reduzidas. Além disso, também oferece um mecanismo integrado de compressão de dados para reduzir ainda mais o uso de memória e uma estrutura de partilha de dados implícita que duplica dados, de forma eficiente, quando necessário recorrendo a uma estratégia copy-on-write. A implementação da solução descrita é disponibilizada na forma de uma biblioteca de software que oferece um framework para a criação de modelos baseados em grelhas volumétricas, e.g. grelhas de ocupação. Como existe uma separação entre o modelo e a gestão de espaço, todas as funcionalidades da abordagem esparsa-densa estão disponíveis para qualquer modelo implementado com o framework. O processo de mapeamento é um problema complexo considerando que localização e mapeamento são resolvidos simultaneamente. Este problema, conhecido como localização e mapeamento simultâneo (SLAM), tem tendência a de consumir recursos consideráveis à medida que a exigência na qualidade do mapeamento aumenta. De modo a contribuir para o aumento da eficiência, esta tese apresenta duas solução de SLAM. Na primeira abordagem, o algoritmo de localização é adaptado ao mapeamento incremental que, em combinação com o framework esparso-denso, oferece uma solução de SLAM online computacionalmente eficiente. O resultados obtidos são comparados com outras soluções disponíveis na literatura recorrendo a um benchmark de SLAM. Os resultados obtidos demonstram que a nossa solução oferece uma boa eficiência sem comprometer a exatidão. A segunda abordagem combina o nosso SLAM online com um filtro de partículas Rao-Blackwellized para propor uma solução de full SLAM com um grau elevado de eficiência computacional. A solução inclui propostas de distribuição melhorada com refinamento de pose através de scan matching, re-amostragem adaptativa com pesos de amostragem suavizados, partilha eficiente de dados entre partículas da mesma geração e suporte para multi-threading.One of the most basic perception problems in robotics is the ability to estimate the pose of a mobile robot relative to the environment. This problem is known as mobile robot localization and its accuracy and efficiency has a direct impact in all systems than depend on localization. In this thesis, we address the localization problem by proposing an algorithm based on scan matching with robust non-linear least squares optimization on a manifold that relies on a continuous likelihood field as measurement model. This solution offers a noticeable improvement in computational efficiency without losing accuracy. Associated with localization is the problem of creating the geometric representation (or map) of the environment using the available measurements, a problem known as mapping. In mapping, the most popular geometric representation is the volumetric grid that quantizes space into cubic volumes of equal size. The regular volumetric grid implementation offers direct and fast access to data but requires a substantial amount of allocated memory. Therefore, in this thesis, we propose a hybrid data structure with sparse division of space combined with dense subdivision of space that offers efficient access times with reduced memory allocation. Additionally, it offers an online data compression mechanism to further reduce memory usage and an implicit data sharing structure that efficiently duplicates data when needed using a thread safe copy-on-write strategy. The implementation of the solution is available as a software library that provides a framework to create models based on volumetric grids, e.g. occupancy grids. The separation between the model and space management makes all features of the sparse-dense approach available to every model implemented with the framework. The process of mapping is a complex problem, considering that localization and mapping have to be solved simultaneously. This problem, known as simultaneous localization and mapping (SLAM), has the tendency to consume considerable resources as the mapping quality requirements increase. As an effort to increase the efficiency of SLAM, this thesis presents two SLAM solutions. The first proposal adapts our localization algorithm to incremental mapping that, in combination with the sparse-dense framework, provides a computationally efficient online SLAM solution. Using a SLAM benchmark, the obtained results are compared with other solutions found in the literature. The comparison shows that our solution provides good efficiency without compromising accuracy. The second approach combines our online SLAM with a Rao-Blackwellized particle filter to propose a highly computationally efficient full SLAM solution. It includes an improved proposal distribution with scan matching pose refinement, adaptive resampling with smoothed importance weight, efficient sharing of data between sibling particles and multithreading support

Commande collaborative d'un fauteuil roulant dans un environnement partiellement connu

RÉSUMÉ Depuis des années, des chercheurs et des étudiants aux cycles supérieurs de trois universités au Québec ont travaillé au projet de réalisation d’un prototype d’un Fauteuil Roulant Motorisé Intelligent (FRMI). C’est un robot mobile équipé de capteurs, de caméras et de modules de contrôle permettant à la chaise roulante d’accomplir de nombreuses tâches autonomes avec assistance aux utilisateurs. Malgré le bon état d’avancement de ces travaux de recherche, il nous reste plusieurs aspects à améliorer et perfectionner. En effet, le fauteuil possède des modules de cartographie et localisation simultanées (Simultaneous Localization And Mapping (SLAM)), de navigation complètement autonome, et de contrôle collaboratif qui assiste les usagers en combinant les commandes de téléopération et autonomes. Pour utiliser le contrôle collaboratif, il faut déterminer une ou plusieurs destinations potentielles dans la carte de l’environnement. Avec les commandes données par l’utilisateur, le système estime son intention et l’aide à atteindre la destination. Pour les personnes âgées, les utilisateurs principaux des fauteuils roulants, il n’est pas concevable de naviguer pour créer la carte de l’environnement, de la lire et d’y placer des destinations potentielles. Pour simplifier la tâche de l’utilisateur, nous devons localiser le véhicule dans une carte connue a priori et utiliser cette carte dans un algorithme de SLAM et inférer automatiquement l’intention de l’utilisateur au cours de la navigation. Le but de ce projet est alors de compléter l’environnement de travail autour du module de contrôle collaboratif en améliorant le module de cartographie et de créer un module qui détermine des destinations potentielles dans la carte afin de les placer automatiquement dans l’environnement de navigation de la chaise. La réalisation de ces objectifs est accomplie par une étude du module de contrôle collaboratif, l’insertion de la carte connue a priori dans un algorithme de SLAM et la détection automatique de points d’intérêt dans cette carte. La première partie consiste à comparer des algorithmes de SLAM existants pour choisir le plus approprié à notre application. D’un autre côté, une technique de construction des données de SLAM à partir d’une carte de l’environnement est implémentée et testée grâce à un algorithme qui crée une carte de type SLAM à partir d’une génération de trajectoires virtuelles.----------ABSTRACT Researchers and students in three universities of Quebec, École Polytechnique de Montréal, University de Montréal and McGill University have been working on the Intelligent Powered Wheelchair (IPW) for many years. A wheelchair which has the ability to do some daily tasks with the help of sensors and computers was developed. It can navigate autonomously or create a map of the environment by itself. Therefore, it can be considered as a mobile robot. Despite the fact that the IPW is in a very advanced state and was developed for years, improvements can still be made. Indeed, the chair possessed a SLAM module to create a map and localize the chair in the environment, a navigation module which can navigate autonomously inside the map or assists the user to control the chair without ignoring their command by combining the autonomous navigation with the manual navigation. It is called shared autonomy. In order to use the shared autonomy, first of all, we need to determine the possible destination on the map. Then, the controller will analyse the manoeuver of the user and after that, it will guess the user’s intention before leading the robot to the destination. For the main user of the wheelchair, the elderly, the utilization of such function is not obvious. In order to simplify the user’s works, we need to locate the robot in the map and then define all the destinations automatically. The project completes the workflow of the shared autonomy control by improving the SLAM algorithm of the IPW and by creating a module which analyses the map and determines all the destinations. In this thesis, we analyse many methods of SLAM available before deciding to implement one in our chair. Second, we will modify the design of the algorithm so we can load a map before navigating by creating a virtual trajectory which navigate the existing map. Finally, we create a module that can find all the destinations in the map and manage to put it in the SLAM all automatically. We want to improve the user experience by simplifying its tasks. Our works can support the shared autonomy algorithm to reach as many patients as possible, even the one who are not used to modern technology

Localização e navegação de um veículo de condução autónoma

Mestrado em Engenharia Electrónica e TelecomunicaçõesA área da condução autónoma tem sido palco de grandes desenvolvimentos nos últimos anos. Não só se tem visto um grande impulso na investigação, existindo já um número considerável de carros autónomos, mas também no mercado, com vários sistemas de condução assistida a equipar veículos comercializados. No trabalho realizado no âmbito desta dissertação, foram abordados e implementados vários tópicos relevantes para condução autónoma. Nomeadamente, foram implementados sistemas de mapeamento, localização e navegação num veículo autónomo dotado de um sistema de locomoção Ackerman. O veículo é capaz de construir o mapa da pista e de usar esse mapa para navegar. O mecanismo de mapeamento é supervisionado, no sentido em que o veículo tem de ser remotamente controlado de modo a cobrir a totalidade da pista. A localização do veículo na pista é realizado usando um filtro de partículas, usando um modelo de movimento adequado ao seu tipo de locomoção. O planeamento de percurso faz-se a dois níveis. A um nível mais alto, definem-se pontos de passagem na pista que estabelecem o percurso geral a realizar pelo veículo. A definição destes pontos está diretamente relacionada com a concretização de tarefas impostas ao veículo. A um nível mais baixo, o percurso entre pontos adjacentes anteriores é detalhado numa sequência mais fina de pontos de passagem que tem em consideração as limitações do modelo Ackerman da locomoção do veículo. A navegação é adaptativa, no sentido em que se adequa à existência de obstáculos, entretanto detetados pelo sistema sensorial do veículo. O sistema sensorial do veículo é essencialmente baseado num dispositivo com visão RGB-D (Kinect) montado num suporte com dois graus de liberdade (pan&tilt). Este sistema é usado concorrentemente para ver a estrada e os obstáculos que nela possam existir e para detetar e identificar sinais de trânsito que aparecem na pista. A aquisição e processamento dos dados sensoriais e a sua transformação em informação (localização do veículo na pista, deteção e localização de obstáculos, deteção e identificação dos sinais de trânsito) foi trabalho realizado pelo autor. Um agente de software foi desenvolvido para gerir o acesso concorrente ao dispositivo de visão. O veículo desenvolvido participou na Competição de Condução Autónoma, do Festival Nacional de Robótica, edição de 2013, tendo alcançado o primeiro lugar.The autonomous driving field has been a stage of major developments in the last years. Not only has been seen a major push in the research, already existing several self driving cars, but also in the market, with several assisted driving systems equipped in commercialized vehicles. In the work developed in the scope of this dissertation, it were approached and developed several relevant topics to the autonomous driving problem. Namely, it were implemented mapping systems, localization and navigation in an autonomous vehicle with an Ackerman locomotion system. The vehicle is capable of building the map of the track and use that map to navigate. The mapping mechanism is supervised, the vehicle has to be remotely controlled to cover the entire track. The localization of the vehicle in the track is accomplished using a particle filter, using the adequate motion model to its locomotion system. The path planning is performed at two levels. At a higher level, the overall course to be performed by the vehicle is defined by passage points. At a lower level, the path between the aforementioned points is detailed in a thiner sequence of points that take into account the limitations of the Ackerman motion model. The navigation is adaptive since it adapts to the existence of the obstacles detected by the robot’s sensory system. The vehicle’s sensory system is essentially based on a device with RGB-D vision system (Kinect) mounted over a structure with two degrees of freedom (pan&tilt). This system is concurrently used to see the track and the obstacles that may exist and to detect and identify traffic signs that appear on the track. The acquisition and processing of the sensory data and its transformation in information (localization of the vehicle in the track, detection and localization of obstacles, detection and identification of traffic signs) was work developed by the author. A software agent was developed to manage the concurrent access to the vision device. The developed vehicle participated in the Autonomous Driving Competition, from the Portuguese Robotics Open, 2013 edition, having achieved the first place

Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

The work reported in this article describes the research advances and the lessons learned by the Robotics, Perception and Real-Time group over a decade of research in the field of ground robotics in confined environments. This study has primarily focused on localization, navigation, and communications in tunnel-like environments. As will be discussed, this type of environment presents several special characteristics that often make well-established techniques fail. The aim is to share, in an open way, the experience, errors, and successes of this group with the robotics community so that those that work in such environments can avoid (some of) the errors made. At the very least, these findings can be readily taken into account when designing a solution, without needing to sift through the technical details found in the papers cited within this text

Computation and Time constraints in Localization and Mapping Problems

Research on simultaneous localization and mapping problems has been extensively carried out by robotics community in the last decade and several subproblems –like data association, map representation, dynamic environments or semantic mapping– have been more or less deeply investigated. One of the most important questions is the online execution of localization and mapping methods. Since observations are periodically captured by robot sensors, localization and mapping algorithms are constrained to complete the execution of an update before a new observation is available. In literature, several partial contributions have been presented, most of them focused on the reduction of computational complexity, but no comprehensive discussion of real-time feasibility had been previously proposed. The reasons that make real-time feasibility difficult are different in the case of localization and of mapping problems, but a general criterion can be found. In this thesis we claim that a locality principle is a general design criterion for real-time or incremental execution of localization and mapping algorithms. The probabilistic robotics paradigm provides a unified formulation for the different problems and a conceptual framework for the application of the proposed criterion. Locality may be applied to perform temporal or spatial decomposition of the global estimation. This thesis provides a general perspective of real-time feasibility and the identification of locality principle as a general design criterion for algorithms to meet time constraints. The particular contributions of this thesis correspond to the application of the locality principle to specific problems. The Real-Time Particle Filter is an advanced version of Particle Filter algorithm conceived to achieve a tradeoff between time constraints and filter accuracy depending on the number of samples. This goal is achieved by partitioning the overall samples required to obtain the required accuracy into sets, each of them corresponding to an observation, and by reconstructing the new set at the end of an estimation window. We proposed two main contributions: first, an analysis of the efficiency of the resampling solution of the Real-Time Particle Filter through the concept of effective sample size; second, a method to compute the mixture weights that balances the the effective sample size of partition sets and is less prone to numerical instability. The second specific contribution is an incremental version of a maximum likelihood map estimator. The adopted technique combines stochastic gradient descent and incremental tree parameterization and exploits an efficient optimization technique and organizes the graph into a spanning tree structure suitable for a decomposition. In this thesis, the incremental version of the original algorithm has been adapted using again the locality principle. Local decomposition is achieved selecting the portion of the network perturbed by the addition of a new constraint. Furthermore, the perturbation of gradient descent iteration is limited for the region already converged by adapting the learning rate. Finally, optimization is scheduled with an heuristic rule that controls the error increase in the constraint network. The constraint solver has been integrated with a map builder that extracts the constraint network from laser scans and represents the environment with a metric-topological hybrid map. While real-time feasibility is not granted, the proposed incremental tree network optimizer is suitable for online execution and the algorithm converges faster than the previous version of the same algorithm and in several condition performs better than other state-of-the-art methods. The final contribution is a parallel maximum likelihood algorithm for robot mapping. The proposed algorithm estimates the map iterating a linearization step and the solution of the linear system with Gauss-Seidel relaxation. The network is divided in connected clusters of local nodes and the reorder induced by this decomposition transforms the linearized information matrix in block-border diagonal form. Each diagonal block of the matrix can then be solved independently. The proposed parallel maximum likelihood algorithm can exploit the computation resources provided by commodity multi-core processor. Moreover, this solution can be applied to multi-robot mapping. The contributions presented in this dissertation outline a novel perspective on real-time feasibility of robot localization and mapping methods, thus bringing these algorithmic techniques closer to applications