Sensordatenfusion zur Robusten und präzisen EKF Lokalisierung von mobilen Robotern

Diese Arbeit beschreibt einen Ansatz zur Lokalisierung von Mobilrobotern mittels der Kombination eines Laserscanners mit monokularem Video. Das Verfahren ist merkmalsbasiert und benutzt ein erweitertes Kalman filter (EKF) zur Datenfusion und Positionsschätzung. Die Umgebungsmerkmale sind Liniensegmente für den Laserscanner und vertikale Kanten für die Kamera. Physikalisch gut basierte Unsicherheitsmodelle beider Sensoren werden eingesetzt und bei Sensorkalibration und Merkmalsextraktion in Betracht gezogen. Dies liefert die geschätzten ersten zwei Momente der Merkmalsvektoren. Die Experimente, die auf einem vollständig autonomen Roboter durchgeführt wurden, zielten auf zwei Fragestellungen ab: In welchem Mass kann das Hinzufügen video-basierter Umgebungsinformation die Navigation hinsichtlich Robustheit und Präzision verbessern? Die dazu ausgeführten Experimente zeigen, dass gerade in schwierigen Lokalisierungsszenarien wie lange Korridore, die Bildinformation einen unerlässlichen Beitrag liefert und in der Lage ist, die Positionsschätzung im allgemeinen und besonders in der Orientierung zu verbessern

Autonomous satellite constellation for enhanced Earth coverage using coupled selection equations

This paper presents a novel solution to the problem of autonomous task allocation for a self-organising constellation of small satellites in Earth orbit. The method allows the constellation members to plan manoeuvres to cluster themselves above particular target longitudes on the Earth’s surface. This is enabled through the use of Coupled Selection Equations, which represent a dynamical systems approach to combinatorial optimisation problems, and whose solution tends towards a Boolean matrix which describes pairings of the satellites and targets which solves the relevant assignment problems. Satellite manoeuvres are actuated using a simple control law which incorporates the results of the Coupled Selection Equations. Three demonstrations of the efficacy of the method are given in order of increasing complexity - first with an equal number of satellites and targets, then with a surplus of satellites, including agent failure events, and finally with a constellation of two different satellite types. The method is shown to provide efficient solutions, whilst being computationally non-intensive, quick to converge and robust to satellite failures. Proposals to extend the method for on-board processing on a distributed architecture are discussed

From Monocular SLAM to Autonomous Drone Exploration

Micro aerial vehicles (MAVs) are strongly limited in their payload and power capacity. In order to implement autonomous navigation, algorithms are therefore desirable that use sensory equipment that is as small, low-weight, and low-power consuming as possible. In this paper, we propose a method for autonomous MAV navigation and exploration using a low-cost consumer-grade quadrocopter equipped with a monocular camera. Our vision-based navigation system builds on LSD-SLAM which estimates the MAV trajectory and a semi-dense reconstruction of the environment in real-time. Since LSD-SLAM only determines depth at high gradient pixels, texture-less areas are not directly observed so that previous exploration methods that assume dense map information cannot directly be applied. We propose an obstacle mapping and exploration approach that takes the properties of our semi-dense monocular SLAM system into account. In experiments, we demonstrate our vision-based autonomous navigation and exploration system with a Parrot Bebop MAV