Probabilistic Self-Localization and Mapping: An Asynchronous Multirate Approach

"© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."[EN] In this paper, we present a set of robust and efficient algorithms with O(N) cost for the solution of the Simultaneous Localization And Mapping (SLAM) problem of a mobile robot. First, we introduce a novel object detection method, which is mainly based on multiple line fitting method for landmark detection with regular constrained angles. Second, a line-based pose estimation method is proposed, based on LeastSquares (LS). This method performs the matching of lines, providing the global pose estimation under assumption of known Data-Association. Finally, we extend the FastSLAM (FActored Solution To SLAM) algorithm for mobile robot self-localisation and mapping by considering the asynchronous sampling of sensors and actuators. In this sense, multi-rate asynchronous holds are used to interface signals with different sampling rates. Moreover, an asynchronous fusion method to predict and update mobile robot pose and map is also presented. In addition to this, FastSLAM 1.0 has been also improved by considering the estimated pose with the LS-approach to re-allocate each particle of the posterior distribution of the robot pose. This approach has a lower computational cost than the original Extended Kalman Filtering (EKF) approach in FastSLAM 2.0. All these methods have been combined in order to perform an efficient and robust self-localization and map building process. Additionally, these methods have been validated with experimental real data, in mobile robot moving on an unknown environment for solving the SLAM problem.This work has been supported by the Spanish Government (MCyT) research project BIA2005-09377-C03-02 and by the Italian Government (MIUR) research project PRIN2005097207.Armesto, L.; Ippoliti, G.; Longhi, S.; Tornero Montserrat, J. (2008). Probabilistic Self-Localization and Mapping: An Asynchronous Multirate Approach. IEEE Robotics & Automation Magazine. 15(2):77-88. https://doi.org/10.1109/M-RA.2007.907355S778815

Multiple-Target Tracking in Complex Scenarios

In this dissertation, we develop computationally efficient algorithms for multiple-target tracking: MTT) in complex scenarios. For each of these scenarios, we develop measurement and state-space models, and then exploit the structure in these models to propose efficient tracking algorithms. In addition, we address design issues such as sensor selection and resource allocation. First, we consider MTT when the targets themselves are moving in a time-varying multipath environment. We develop a sparse-measurement model that allows us to exploit the inherent joint delay-Doppler diversity offered by the environment. We then reformulate the problem of MTT as a block-support recovery problem using the sparse measurement model. We exploit the structure of the dictionary matrix to develop a computationally efficient block support recovery algorithm: and thereby a multiple-target tracking algorithm) under the assumption that the channel state describing the time-varying multipath environment is known. Further, we also derive an upper bound on the overall error probability of wrongly identifying the support of the sparse signal. We then relax the assumption that the channel state is known. We develop a new particle filter called the Multiple Rao-Blackwellized Particle Filter: MRBPF) to jointly estimate both the target and the channel states. We also compute the posterior Cramér-Rao bound: PCRB) on the estimates of the target and the channel states and use the PCRB to find a suitable subset of antennas to be used for transmission in each tracking interval, as well as the power transmitted by these antennas. Second, we consider the problem of tracking an unknown number and types of targets using a multi-modal sensor network. In a multi-modal sensor network, different quantities associated with the same state are measured using sensors of different kinds. Hence, an efficient method that can suitably combine the diverse information measured by each sensor is required. We first develop a Hierarchical Particle Filter: HPF) to estimate the unknown state from the multi-modal measurements for a special class of problems which can be modeled hierarchically. We then model our problem of tracking using a hierarchical model and then use the proposed HPF for joint initiation, termination and tracking of multiple targets. The multi-modal data consists of the measurements collected from a radar, an infrared camera and a human scout. We also propose a unified framework for multi-modal sensor management that comprises sensor selection: SS), resource allocation: RA) and data fusion: DF). Our approach is inspired by the trading behavior of economic agents in commercial markets. We model the sensors and the sensor manager as economic agents, and the interaction among them as a double sided market with both consumers and producers. We propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of SS, RA and DF. Third, we address MTT problem in the presence of data association ambiguity that arises due to clutter. Data association corresponds to the problem of assigning a measurement to each target. We treat the data association and state estimation as separate subproblems. We develop a game-theoretic framework to solve the data association, in which we model each tracker as a player and the set of measurements as strategies. We develop utility functions for each player, and then use a regret-based learning algorithm to find the correlated equilibrium of this game. The game-theoretic approach allows us to associate measurements to all the targets simultaneously. We then use particle filtering on the reduced dimensional state of each target, independently

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

Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process

Graphical models for visual object recognition and tracking

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 277-301).We develop statistical methods which allow effective visual detection, categorization, and tracking of objects in complex scenes. Such computer vision systems must be robust to wide variations in object appearance, the often small size of training databases, and ambiguities induced by articulated or partially occluded objects. Graphical models provide a powerful framework for encoding the statistical structure of visual scenes, and developing corresponding learning and inference algorithms. In this thesis, we describe several models which integrate graphical representations with nonparametric statistical methods. This approach leads to inference algorithms which tractably recover high-dimensional, continuous object pose variations, and learning procedures which transfer knowledge among related recognition tasks. Motivated by visual tracking problems, we first develop a nonparametric extension of the belief propagation (BP) algorithm. Using Monte Carlo methods, we provide general procedures for recursively updating particle-based approximations of continuous sufficient statistics. Efficient multiscale sampling methods then allow this nonparametric BP algorithm to be flexibly adapted to many different applications.(cont.) As a particular example, we consider a graphical model describing the hand's three-dimensional (3D) structure, kinematics, and dynamics. This graph encodes global hand pose via the 3D position and orientation of several rigid components, and thus exposes local structure in a high-dimensional articulated model. Applying nonparametric BP, we recover a hand tracking algorithm which is robust to outliers and local visual ambiguities. Via a set of latent occupancy masks, we also extend our approach to consistently infer occlusion events in a distributed fashion. In the second half of this thesis, we develop methods for learning hierarchical models of objects, the parts composing them, and the scenes surrounding them. Our approach couples topic models originally developed for text analysis with spatial transformations, and thus consistently accounts for geometric constraints. By building integrated scene models, we may discover contextual relationships, and better exploit partially labeled training images. We first consider images of isolated objects, and show that sharing parts among object categories improves accuracy when learning from few examples.(cont.) Turning to multiple object scenes, we propose nonparametric models which use Dirichlet processes to automatically learn the number of parts underlying each object category, and objects composing each scene. Adapting these transformed Dirichlet processes to images taken with a binocular stereo camera, we learn integrated, 3D models of object geometry and appearance. This leads to a Monte Carlo algorithm which automatically infers 3D scene structure from the predictable geometry of known object categories.by Erik B. Sudderth.Ph.D

Survey of Bayesian Models for Modelling of Stochastic Temporal Processes

This survey gives an overview of popular generative models used in the modeling of stochastic temporal systems. In particular, this survey is organized into two parts. The first part discusses the discrete-time representations of dynamic Bayesian networks and dynamic relational probabilistic models, while the second part discusses the continuous-time representation of continuous-time Bayesian networks