Appearance-based Indoor Navigation by IBVS using Line Segments

Also presented in IEEE Int. Conf. on Robotics and Automation, Stockolm, SwedenInternational audienc

Keyframe-based monocular SLAM: design, survey, and future directions

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery

Secure indoor navigation and operation of mobile robots

In future work environments, robots will navigate and work side by side to humans. This raises big challenges related to the safety of these robots. In this Dissertation, three tasks have been realized: 1) implementing a localization and navigation system based on StarGazer sensor and Kalman filter; 2) realizing a human-robot interaction system using Kinect sensor and BPNN and SVM models to define the gestures and 3) a new collision avoidance system is realized. The system works on generating the collision-free paths based on the interaction between the human and the robot.In zukünftigen Arbeitsumgebungen werden Roboter navigieren nebeneinander an Menschen. Das wirft Herausforderungen im Zusammenhang mit der Sicherheit dieser Roboter auf. In dieser Dissertation drei Aufgaben realisiert: 1. Implementierung eines Lokalisierungs und Navigationssystem basierend auf Kalman Filter: 2. Realisierung eines Mensch-Roboter-Interaktionssystem mit Kinect und AI zur Definition der Gesten und 3. ein neues Kollisionsvermeidungssystem wird realisiert. Das System arbeitet an der Erzeugung der kollisionsfreien Pfade, die auf der Wechselwirkung zwischen dem Menschen und dem Roboter basieren

A Unified Hybrid Formulation for Visual SLAM

Visual Simultaneous Localization and Mapping (Visual SLAM (VSLAM)), is the process of estimating the six degrees of freedom ego-motion of a camera, from its video feed, while simultaneously constructing a 3D model of the observed environment. Extensive research in the field for the past two decades has yielded real-time and efficient algorithms for VSLAM, allowing various interesting applications in augmented reality, cultural heritage, robotics and the automotive industry, to name a few. The underlying formula behind VSLAM is a mixture of image processing, geometry, graph theory, optimization and machine learning; the theoretical and practical development of these building blocks led to a wide variety of algorithms, each leveraging different assumptions to achieve superiority under the presumed conditions of operation. An exhaustive survey on the topic outlined seven main components in a generic VSLAM pipeline, namely: the matching paradigm, visual initialization, data association, pose estimation, topological/metric map generation, optimization, and global localization. Before claiming VSLAM a solved problem, numerous challenging subjects pertaining to robustness in each of the aforementioned components have to be addressed; namely: resilience to a wide variety of scenes (poorly textured or self repeating scenarios), resilience to dynamic changes (moving objects), and scalability for long-term operation (computational resources awareness and management). Furthermore, current state-of-the art VSLAM pipelines are tailored towards static, basic point cloud reconstructions, an impediment to perception applications such as path planning, obstacle avoidance and object tracking. To address these limitations, this work proposes a hybrid scene representation, where different sources of information extracted solely from the video feed are fused in a hybrid VSLAM system. The proposed pipeline allows for seamless integration of data from pixel-based intensity measurements and geometric entities to produce and make use of a coherent scene representation. The goal is threefold: 1) Increase camera tracking accuracy under challenging motions, 2) improve robustness to challenging poorly textured environments and varying illumination conditions, and 3) ensure scalability and long-term operation by efficiently maintaining a global reusable map representation