Localization of autonomous ground vehicles in dense urban environments

The localization of autonomous ground vehicles in dense urban environments poses a challenge. Applications in classical outdoor robotics rely on the availability of GPS systems in order to estimate the position. However, the presence of complex building structures in dense urban environments hampers a reliable localization based on GPS. Alternative approaches have to be applied In order to tackle this problem. This thesis proposes an approach which combines observations of a single perspective camera and odometry in a probabilistic framework. In particular, the localization in the space of appearance is addressed. First, a topological map of reference places in the environment is built. Each reference place is associated with a set of visual features. A feature selection is carried out in order to obtain distinctive reference places. The topological map is extended to a hybrid representation by the use of metric information from Geographic Information Systems (GIS) and satellite images. The localization is solved in terms of the recognition of reference places. A particle lter implementation incorporating this and the vehicle's odometry is presented. The proposed system is evaluated based on multiple experiments in exemplary urban environments characterized by high building structures and a multitude of dynamic objects

The HYbrid Metric Maps (HYMMS): A novel map representation for denseSLAM

Abstract — This paper presents a new hybrid metric map representation (HYMM) that combines feature maps with other dense metric sensory information. The global feature map is partitioned into a set of connected Local Triangular Regions (LTRs), which provide a reference for a detailed multi-dimensional description of the environment. The HYMM framework permits the combination of efficient feature-based SLAM algorithms for localisation with, for example, occupancy grid (OG) maps. This fusion of feature and grid maps has several complementary properties; for example, grid maps can assist data association and can facilitate the extraction and incorporation of new landmarks as they become identified from multiple vantage points. The representation presented here will allow the robot to perform DenseSLAM. DenseSLAM is the process of performing SLAM whilst obtaining a dense environment representation. I

Fusion de données multi-capteurs pour la construction incrémentale du modèle tridimensionnel texturé d'un environnement intérieur par un robot mobile

Ce travail traite la Modélisation 3D d'un environnement intérieur par un robot mobile. La principale contribution concerne la construction d'un modèle géométrique hétérogène combinant des amers plans texturés, des lignes 3D et des points d'intérêt. Pour cela, nous devons fusionner des données géométriques et photométriques. Ainsi, nous avons d'abord amélioré la stéréovision, en proposant une approche de la mise en correspondance stéréoscopique par coupure de graphe. Notre contribution réside dans la construction d'un graphe réduit qui a permis d'accélérer la méthode globale et d'obtenir de meilleurs résultats que les méthodes locales. Aussi, pour percevoir l'environnement, le robot est équipé d'un télémètre laser 3D et d'une caméra. Nous proposons une chaîne algorithmique permettant de construire une carte hétérogène, par l'algorithme de Cartographie et Localisation Simultanées (EKF-SLAM). Le placage de la texture sur les facettes planes a permis de solidifier l'association de données.This thesis examines the problem of 3D Modelling of indoor environment by a mobile robot. Our main contribution consists in constructing a heterogeneous geometrical model containing textured planar landmarks, 3D lines and interest points. For that, we must fuse geometrical and photometrical data. Hence, we began by improving the stereo vision algorithm, and proposed a new approach of stereo matching by graph cuts. The most significant contribution is the construction of a reduced graph that allows to accelerate the global method and to provide better results than the local methods. Also, to perceive the environment, the robot is equipped by a 3D laser scanner and by a camera. We proposed an algorithmic chain allowing to incrementally constructing a heterogeneous map, using the algorithm of Simultaneous Localization and Mapping based (EKF-SLAM). Mapping the texture on the planar landmarks makes more robust the phase of data association

Sensor Data Fusion and Image Processing for Object and Hazard Detection

Schwerpunkte der vorliegenden Arbeit sind die automatische Erkennung und Verfolgung von Objekten in Fahrsituationen sowie die Ableitung von potentiellen Gefahren. Hierzu werden die Daten eines Laserscanners und einer Kamera verarbeitet und fusioniert. Die Arbeit stellt neue Methoden der unmittelbaren Umfelderfassung dar und dient als Grundlage für innovative Assistenz- und Automationssysteme im Fahrzeug. Solche Systeme unterstützen den Fahrer zur Erhöhung der Fahrsicherheit, der Verkehrseffizienz und des Komforts. Die entwickelten Methoden sind auf unterschiedlichen Abstraktionsebenen angesiedelt: Auf Sensordatenebene werden die Daten vorbereitet und reduziert. Insbesondere liegt der Fokus auf der Erkennung von Fahrschwankungen aus Kamerabildern und auf der Erkennung des Fahrkorridors als Interessenbereich aus den Daten mehrerer Sensoren. Auf Objektebene findet die zentrale Datenfusion statt. Durch die Auswahl einer konkurrierenden Objektfusion wird eine hohe Sensorunabhängigkeit, Zuverlässigkeit und Verfügbarkeit erreicht. Hierzu werden im Vorfeld Objektbeobachtungen aus beiden Sensoren extrahiert. Diese werden zur Objekterkennung und -verfolgung fusioniert, mit besonderem Fokus auf die Robustheit gegenüber manövrierenden Objekten, Messausreißern, split- und merge-Effekten und der partiellen Beobachtbarkeit der Objekte. Auf der Anwendungsebene der Arbeit wird die frühzeitige Erkennung von potentiellen Gefahrensituationen vorgestellt. Hierzu wurde ein statistischer Ansatz entwickelt, in dem Gefahren als atypische Situationen behandelt werden. Dieser allgemeingültige und erweiterbare Ansatz wird beispielhaft auf Basis der Objektdaten umgesetzt. Die vorgestellten Ansätze wurden systematisch entwickelt, prototypisch und modular implementiert sowie mit simulierten und realen Daten getestet. Die Ergebnisse zeigen hierbei eine Steigerung der Qualität und Robustheit der Daten, so dass ein wichtiger Beitrag zur Verbesserung von Assistenz und Automation geleistet wird.The present work deals with automatic detection and tracking of objects in driving situations as well as derivation of potential hazards. To do this, the data of a laser scanner and a camera is processed and fused. The work provides new methods in the area of immediate environment detection and modeling. Thus, it creates a basis for innovative driver assistance and automation systems. The aim of such systems is to improve driving safety, traffic efficiency and driving comfort. The methods introduced in this work can be classified into different abstraction levels: At sensor data level, the data is prepared and reduced. In this work, the focus is especially set on the detection of driving oscillations from camera images and on the detection of the driving corridor from the data of different sensors, used later as the primary area of interest. At object level the central data fusion is done. High reliability, availability and sensor independency are achieved by choosing a competitive object fusion approach. As an input of the data fusion, object observations from camera and laser scanner data are extracted. These are then fused at the aim of object detection and tracking, where aspects such as robustness against manoeuvring objects, measurement outliers, split and merge effects, as well as partial object observability are addressed. At application level, early detection of potential hazards is addressed. A statistical approach has been chosen and developed, in which hazards are handled as atypical situations. This general and expandable approach is exemplarily shown based on the detected object data. The presented strategies and methods have been developed systematically, implemented in a modular prototype and tested with simulated and real data. The test results of the data fusion system show a win in data quality and robustness, with which an improvement of driver assistance and automation systems can be reached

Agent-Based Lost Person Movement Modelling, Prediction and Search in Wilderness

In this research we investigate the problem of searching for a Lost Person (LP) in wilderness using an autonomous Unmanned Aerial Vehicle (UAV). The problem of search with a UAV is often treated as gridded environment search where the state of each grid (cell) is examined individually for the presence or absence of the target. However, this idealised way of search fails to exploit many potentially valuable dependencies and secondary cues — such as material deposited or left by the LP or topographical features such as natural tracks (trails) — which could significantly aid the search process. We discuss the need for such a system and review the current state-of-the-art work. Since key to a quick and successful search is a well defined initial distributions. We further argue the need to generate the initial distribution over the trajectory of the LP, not merely the end location, usually done in literature. We propose a search framework consisting of three key phases: information gathering, initial distribution generation and search. In the information gathering phase, we collect detailed information related to both the LP and the search environment. Then in the initial distribution generation phase, using the information gathered, we generate distribution over the LP’s trajectory using particles. Each particle represented by an agent model of LP movement with sampled parameters, navigating and interacting with the environment represented using data-sets in the form of terrain elevation, topography and vegetation. To ensure, the agent model is a good representation of the LP behaviour, we calibrate its parameters using the method called SMC2 . Finally in the Search phase, a UAV is deployed to explore the search area and detect the LP, any evidence features or changes in the environment. All information detected are localised and used to update the distribution over the LP trail until either the LP is located or the search is terminated