Language-based Feedback Control Using Monte Carlo Sensing *

Abstract-Landmark-based graphs are a useful and parsimonious tool for representing large scale environments. Relating landmarks by means of feedback-control algorithms encoded in a motion description language provides a level of abstraction that enables autonomous vehicles to navigate effectively by composing strings in the language to form complex strategies that would be difficult to design at the level of sensors and actuators. In such a setting, feedback control requires one to pay attention not only to sensor and actuator uncertainty, but also to the ambiguity introduced by the fact that many landmarks may look similar when using a modest set of observations. This work discusses the generation of language-based feedback control sequences for landmark-based navigation together with the problem of sensing landmarks sufficiently well to make feedback meaningful. The paper makes two contributions. First, we extend previous work to include the costs of sensing with varying degrees of accuracy. Second, we describe a Monte Carlo based approach to landmark sensing which relies on the use of particle filters. We include simulation results that illustrate our approach

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

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

Robot environment learning with a mixed-linear probabilistic state-space model

This thesis proposes the use of a probabilistic state-space model with mixed-linear dynamics for learning to predict a robot's experiences. It is motivated by a desire to bridge the gap between traditional models with predefined objective semantics on the one hand, and the biologically-inspired "black box" behavioural paradigm on the other. A novel EM-type algorithm for the model is presented, which is less compuationally demanding than the Monte Carlo techniques developed for use in (for example) visual applications. The algorithm's E-step is slightly approximative, but an extension is described which would in principle make it asymptotically correct. Investigation using synthetically sampled data shows that the uncorrected E-step can any case make correct inferences about quite complicated systems. Results collected from two simulated mobile robot environments support the claim that mixed-linear models can capture both discontinuous and continuous structure in world in an intuitively natural manner; while they proved to perform only slightly better than simpler autoregressive hidden Markov models on these simple tasks, it is possible to claim tentatively that they might scale more effectively to environments in which trends over time played a larger role. Bayesian confidence regions—easily by mixed-linear model— proved be an effective guard for preventing it from making over-confident predictions outside its area of competence. A section on future extensions discusses how the model's easy invertibility could be harnessed to the ultimate aim of choosing actions, from a continuous space of possibilities, which maximise the robot's expected payoff over several steps into the futur

Self–organised multi agent system for search and rescue operations

Autonomous multi-agent systems perform inadequately in time critical missions, while they tend to explore exhaustively each location of the field in one phase with out selecting the pertinent strategy. This research aims to solve this problem by introducing a hierarchy of exploration strategies. Agents explore an unknown search terrain with complex topology in multiple predefined stages by performing pertinent strategies depending on their previous observations. Exploration inside unknown, cluttered, and confined environments is one of the main challenges for search and rescue robots inside collapsed buildings. In this regard we introduce our novel exploration algorithm for multi–agent system, that is able to perform a fast, fair, and thorough search as well as solving the multi–agent traffic congestion. Our simulations have been performed on different test environments in which the complexity of the search field has been defined by fractal dimension of Brownian movements. The exploration stages are depicted as defined arenas of National Institute of Standard and Technology (NIST). NIST introduced three scenarios of progressive difficulty: yellow, orange, and red. The main concentration of this research is on the red arena with the least structure and most challenging parts to robot nimbleness

Statistical modelling of algorithms for signal processing in systems based on environment perception

One cornerstone for realising automated driving systems is an appropriate handling of uncertainties in the environment perception and situation interpretation. Uncertainties arise due to noisy sensor measurements or the unknown future evolution of a traffic situation. This work contributes to the understanding of these uncertainties by modelling and propagating them with parametric probability distributions

Visual Homing in Dynamic Indoor Environments

Institute of Perception, Action and BehaviourOur dissertation concerns robotic navigation in dynamic indoor environments using image-based visual homing. Image-based visual homing infers the direction to a goal location S from the navigator’s current location C using the similarity between panoramic images IS and IC captured at those locations. There are several ways to compute this similarity. One of the contributions of our dissertation is to identify a robust image similarity measure – mutual image information – to use in dynamic indoor environments. We crafted novel methods to speed the computation of mutual image information with both parallel and serial processors and demonstrated that these time-savers had little negative effect on homing success. Image-based visual homing requires a homing agent tomove so as to optimise themutual image information signal. As the mutual information signal is corrupted by sensor noise we turned to the stochastic optimisation literature for appropriate optimisation algorithms. We tested a number of these algorithms in both simulated and real dynamic laboratory environments and found that gradient descent (with gradients computed by one-sided differences) works best

Robust and Accurate Camera Localisation at a Large Scale

The task of camera-based localization aims to quickly and precisely pinpoint at which location (and viewing direction) the image was taken, against a pre-stored large-scale map of the environment. This technique can be used in many 3D computer vision applications, e.g., AR/VR and autonomous driving. Mapping the world is the first step to enable camera-based localization since a pre-stored map serves as a reference for a query image/sequence. In this thesis, we exploit three readily available sources: (i) satellite images; (ii) ground-view images; (iii) 3D points cloud. Based on the above three sources, we propose solutions to localize a query camera both effectively and efficiently, i.e., accurately localizing a query camera under a variety of lighting and viewing conditions within a small amount of time. The main contributions are summarized as follows. In chapter 3, we separately present a minimal 4-point and 2-point solver to estimate a relative and absolute camera pose. The core idea is exploiting the vertical direction from IMU or vanishing point to derive a closed-form solution of a quartic equation and a quadratic equation for the relative and absolute camera pose, respectively. In chapter 4, we localize a ground-view query image against a satellite map. Inspired by the insight that humans commonly use orientation information as an important cue for spatial localization, we propose a method that endows deep neural networks with the 'commonsense' of orientation. We design a Siamese network that explicitly encodes each pixel's orientation of the ground-view and satellite images. Our method boosts the learned deep features' discriminative power, outperforming all previous methods. In chapter 5, we localize a ground-view query image against a ground-view image database. We propose a representation learning method having higher location-discriminating power. The core idea is learning discriminative image embedding. Similarities among intra-place images (viewing the same landmarks) are maximized while similarities among inter-place images (viewing different landmarks) are minimized. The method is easy to implement and pluggable into any CNN. Experiments show that our method outperforms all previous methods. In chapter 6, we localize a ground-view query image against a large-scale 3D points cloud with visual descriptors. To address the ambiguities in direct 2D--3D feature matching, we introduce a global matching method that harnesses global contextual information exhibited both within the query image and among all the 3D points in the map. The core idea is to find the optimal 2D set to 3D set matching. Tests on standard benchmark datasets show the effectiveness of our method. In chapter 7, we localize a ground-view query image against a 3D points cloud with only coordinates. The problem is also known as blind Perspective-n-Point. We propose a deep CNN model that simultaneously solves for both the 6-DoF absolute camera pose and 2D--3D correspondences. The core idea is extracting point-wise 2D and 3D features from their coordinates and matching 2D and 3D features effectively in a global feature matching module. Extensive tests on both real and simulated data have shown that our method substantially outperforms existing approaches. Last, in chapter 8, we study the potential of using 3D lines. Specifically, we study the problem of aligning two partially overlapping 3D line reconstructions in Euclidean space. This technique can be used for localization with respect to a 3D line database when query 3D line reconstructions are available (e.g., from stereo triangulation). We propose a neural network, taking Pluecker representations of lines as input, and solving for line-to-line matches and estimate a 6-DoF rigid transformation. Experiments on indoor and outdoor datasets show that our method's registration (rotation and translation) precision outperforms baselines significantly