Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data

Localization is a key requirement for mobile robot autonomy and human-robot interaction. Vision-based localization is accurate and flexible, however, it incurs a high computational burden which limits its application on many resource-constrained platforms. In this paper, we address the problem of performing real-time localization in large-scale 3D point cloud maps of ever-growing size. While most systems using multi-modal information reduce localization time by employing side-channel information in a coarse manner (eg. WiFi for a rough prior position estimate), we propose to inter-weave the map with rich sensory data. This multi-modal approach achieves two key goals simultaneously. First, it enables us to harness additional sensory data to localise against a map covering a vast area in real-time; and secondly, it also allows us to roughly localise devices which are not equipped with a camera. The key to our approach is a localization policy based on a sequential Monte Carlo estimator. The localiser uses this policy to attempt point-matching only in nodes where it is likely to succeed, significantly increasing the efficiency of the localization process. The proposed multi-modal localization system is evaluated extensively in a large museum building. The results show that our multi-modal approach not only increases the localization accuracy but significantly reduces computational time.Comment: Presented at IEEE-RAS International Conference on Humanoid Robots (Humanoids) 201

Robot Navigation in Human Environments

For the near future, we envision service robots that will help us with everyday chores in home, office, and urban environments. These robots need to work in environments that were designed for humans and they have to collaborate with humans to fulfill their tasks. In this thesis, we propose new methods for communicating, transferring knowledge, and collaborating between humans and robots in four different navigation tasks. In the first application, we investigate how automated services for giving wayfinding directions can be improved to better address the needs of the human recipients. We propose a novel method based on inverse reinforcement learning that learns from a corpus of human-written route descriptions what amount and type of information a route description should contain. By imitating the human teachers' description style, our algorithm produces new route descriptions that sound similarly natural and convey similar information content, as we show in a user study. In the second application, we investigate how robots can leverage background information provided by humans for exploring an unknown environment more efficiently. We propose an algorithm for exploiting user-provided information such as sketches or floor plans by combining a global exploration strategy based on the solution of a traveling salesman problem with a local nearest-frontier-first exploration scheme. Our experiments show that the exploration tours are significantly shorter and that our system allows the user to effectively select the areas that the robot should explore. In the second part of this thesis, we focus on humanoid robots in home and office environments. The human-like body plan allows humanoid robots to navigate in environments and operate tools that were designed for humans, making humanoid robots suitable for a wide range of applications. As localization and mapping are prerequisites for all navigation tasks, we first introduce a novel feature descriptor for RGB-D sensor data and integrate this building block into an appearance-based simultaneous localization and mapping system that we adapt and optimize for the usage on humanoid robots. Our optimized system is able to track a real Nao humanoid robot more accurately and more robustly than existing approaches. As the third application, we investigate how humanoid robots can cover known environments efficiently with their camera, for example for inspection or search tasks. We extend an existing next-best-view approach by integrating inverse reachability maps, allowing us to efficiently sample and check collision-free full-body poses. Our approach enables the robot to inspect as much of the environment as possible. In our fourth application, we extend the coverage scenario to environments that also include articulated objects that the robot has to actively manipulate to uncover obstructed regions. We introduce algorithms for navigation subtasks that run highly parallelized on graphics processing units for embedded devices. Together with a novel heuristic for estimating utility maps, our system allows to find high-utility camera poses for efficiently covering environments with articulated objects. All techniques presented in this thesis were implemented in software and thoroughly evaluated in user studies, simulations, and experiments in both artificial and real-world environments. Our approaches advance the state of the art towards universally usable robots in everyday environments.Roboternavigation in menschlichen Umgebungen In naher Zukunft erwarten wir Serviceroboter, die uns im Haushalt, im Büro und in der Stadt alltägliche Arbeiten abnehmen. Diese Roboter müssen in für Menschen gebauten Umgebungen zurechtkommen und sie müssen mit Menschen zusammenarbeiten um ihre Aufgaben zu erledigen. In dieser Arbeit schlagen wir neue Methoden für die Kommunikation, Wissenstransfer und Zusammenarbeit zwischen Menschen und Robotern bei Navigationsaufgaben in vier Anwendungen vor. In der ersten Anwendung untersuchen wir, wie automatisierte Dienste zur Generierung von Wegbeschreibungen verbessert werden können, um die Beschreibungen besser an die Bedürfnisse der Empfänger anzupassen. Wir schlagen eine neue Methode vor, die inverses bestärkendes Lernen nutzt, um aus einem Korpus von von Menschen geschriebenen Wegbeschreibungen zu lernen, wie viel und welche Art von Information eine Wegbeschreibung enthalten sollte. Indem unser Algorithmus den Stil der Wegbeschreibungen der menschlichen Lehrer imitiert, kann der Algorithmus neue Wegbeschreibungen erzeugen, die sich ähnlich natürlich anhören und einen ähnlichen Informationsgehalt vermitteln, was wir in einer Benutzerstudie zeigen. In der zweiten Anwendung untersuchen wir, wie Roboter von Menschen bereitgestellte Hintergrundinformationen nutzen können, um eine bisher unbekannte Umgebung schneller zu erkunden. Wir schlagen einen Algorithmus vor, der Hintergrundinformationen wie Gebäudegrundrisse oder Skizzen nutzt, indem er eine globale Explorationsstrategie basierend auf der Lösung eines Problems des Handlungsreisenden kombiniert mit einer lokalen Explorationsstrategie. Unsere Experimente zeigen, dass die Erkundungstouren signifikant kürzer werden und dass der Benutzer mit unserem System effektiv die zu erkundenden Regionen spezifizieren kann. Der zweite Teil dieser Arbeit konzentriert sich auf humanoide Roboter in Umgebungen zu Hause und im Büro. Der menschenähnliche Körperbau ermöglicht es humanoiden Robotern, in Umgebungen zu navigieren und Werkzeuge zu benutzen, die für Menschen gebaut wurden, wodurch humanoide Roboter für vielfältige Aufgaben einsetzbar sind. Da Lokalisierung und Kartierung Grundvoraussetzungen für alle Navigationsaufgaben sind, führen wir zunächst einen neuen Merkmalsdeskriptor für RGB-D-Sensordaten ein und integrieren diesen Baustein in ein erscheinungsbasiertes simultanes Lokalisierungs- und Kartierungsverfahren, das wir an die Besonderheiten von humanoiden Robotern anpassen und optimieren. Unser System kann die Position eines realen humanoiden Roboters genauer und robuster verfolgen, als es mit existierenden Ansätzen möglich ist. Als dritte Anwendung untersuchen wir, wie humanoide Roboter bekannte Umgebungen effizient mit ihrer Kamera abdecken können, beispielsweise zu Inspektionszwecken oder zum Suchen eines Gegenstands. Wir erweitern ein bestehendes Verfahren, das die nächstbeste Beobachtungsposition berechnet, durch inverse Erreichbarkeitskarten, wodurch wir kollisionsfreie Ganzkörperposen effizient generieren und prüfen können. Unser Ansatz ermöglicht es dem Roboter, so viel wie möglich von der Umgebung zu untersuchen. In unserer vierten Anwendung erweitern wir dieses Szenario um Umgebungen, die auch bewegbare Gegenstände enthalten, die der Roboter aktiv bewegen muss um verdeckte Regionen zu sehen. Wir führen Algorithmen für Teilprobleme ein, die hoch parallelisiert auf Grafikkarten von eingebetteten Systemen ausgeführt werden. Zusammen mit einer neuen Heuristik zur Schätzung von Nutzenkarten ermöglicht dies unserem System Beobachtungspunkte mit hohem Nutzen zu finden, um Umgebungen mit bewegbaren Objekten effizient zu inspizieren. Alle vorgestellten Techniken wurden in Software implementiert und sorgfältig evaluiert in Benutzerstudien, Simulationen und Experimenten in künstlichen und realen Umgebungen. Unsere Verfahren bringen den Stand der Forschung voran in Richtung universell einsetzbarer Roboter in alltäglichen Umgebungen

Object Detection and Collaboration in Heterogeneous Multi-Robot Systems

With the development of novel technologies, robots are becoming more widely used in everyday applications. Compared with a single robot, Multi-Robot Systems (MRS) are more powerful and cost effective. This thesis will introduce a complete heterogeneous distributed MRS which contains an autonomous mobile robot (Pioneer 3-DX) and a stationary humanoid robot (Baxter), which are able to jointly perform a task that involves navigation and object manipulation. The contributions of this thesis are: 1) we developed an object detection system, locating the pose of objects with a depthcamera through OpenCV and the HSV color model; 2) we optimized a path planning and control system which would enable the Pioneer 3-DX to map its surrounding environment and navigate to target poses, as well as allow the Baxter to grasp, lift and drop the target objects using the MoveIt framework accurately and successfully; 3) we developed a distributed communication system so as to allow two different functional robots to communicate with each other through ROS 2. These contributions are validated with a heterogeneous robot team, including an autonomous mobile robot and a humanoid robot, completing tasks collaboratively

Agent and object aware tracking and mapping methods for mobile manipulators

The age of the intelligent machine is upon us. They exist in our factories, our warehouses, our military, our hospitals, on our roads, and on the moon. Most of these things we call robots. When placed in a controlled or known environment such as an automotive factory or a distribution warehouse they perform their given roles with exceptional efficiency, achieving far more than is within reach of a humble human being. Despite the remarkable success of intelligent machines in such domains, they have yet to make a full-hearted deployment into our homes. The missing link between the robots we have now and the robots that are soon to come to our houses is perception. Perception as we mean it here refers to a level of understanding beyond the collection and aggregation of sensory data. Much of the available sensory information is noisy and unreliable, our homes contain many reflective surfaces, repeating textures on large flat surfaces, and many disruptive moving elements, including humans. These environments change over time, with objects frequently moving within and between rooms. This idea of change in an environment is fundamental to robotic applications, as in most cases we expect them to be effectors of such change. We can identify two particular challenges1 that must be solved for robots to make the jump to less structured environments - how to manage noise and disruptive elements in observational data, and how to understand the world as a set of changeable elements (objects) which move over time within a wider environment. In this thesis we look at one possible approach to solving each of these problems. For the first challenge we use proprioception aboard a robot with an articulated arm to handle difficult and unreliable visual data caused both by the robot and the environment. We use sensor data aboard the robot to improve the pose tracking of a visual system when the robot moves rapidly, with high jerk, or when observing a scene with little visual variation. For the second challenge, we build a model of the world on the level of rigid objects, and relocalise them both as they change location between different sequences and as they move. We use semantics, image keypoints, and 3D geometry to register and align objects between sequences, showing how their position has moved between disparate observations.Open Acces

Tiny-YOLO distance measurement and object detection coordination system for the BarelangFC robot

A humanoid robot called BarelangFC was designed to take part in the Kontes Robot Indonesia (KRI) competition, in the robot coordination division. In this division, each robot is expected to recognize its opponents and to pass the ball towards a team member to establish coordination between the robots. In order to achieve this team coordination, a fast and accurate system is needed to detect and estimate the other robot’s position in real time. Moreover, each robot has to estimate its team members’ locations based on its camera reading, so that the ball can be passed without error. This research proposes a Tiny-YOLO deep learning method to detect the location of a team member robot and presents a real-time coordination system using a ZED camera. To establish the coordinate system, the distance between the robots was estimated using a trigonometric equation to ensure that the robot was able to pass the ball towards another robot. To verify our method, real-time experiments was carried out using an NVDIA Jetson NX Xavier, and the results showed that the robot could estimate the distance correctly before passing the ball toward another robot