An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Basic Principles and Specific Characteristics <s> Surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment. <s> BIB001 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Basic Principles and Specific Characteristics <s> Recent work in the object recognition community has yielded a class of interest point-based features that are stable under significant changes in scale, viewpoint, and illumination, making them ideally suited to landmark-based navigation. Although many such features may be visible in a given view of the robot's environment, only a few such features are necessary to estimate the robot's position and orientation. In this paper, we address the problem of automatically selecting, from the entire set of features visible in the robot's environment, the minimum (optimal) set by which the robot can navigate its environment. Specifically, we decompose the world into a small number of maximally sized regions such that at each position in a given region, the same small set of features is visible. We introduce a novel graph theoretic formulation of the problem and prove that it is NP-complete. Next, we introduce a number of approximation algorithms and evaluate them on both synthetic and real data. <s> BIB002 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Basic Principles and Specific Characteristics <s> In many applications, mobile robots must be able to localize themselves with respect to environments which are not known a priori in order to navigate and accomplish tasks. This means that the robot must be able to build a map of an unknown environment while simultaneously localizing itself within that map. The so called Simultaneous Localization and Mapping or SLAM problem is a formulation of this requirement, and has been the subject of a considerable amount of robotics research in the last decade. ::: This thesis looks at the problem of localization and mapping when the only information available to the robot is measurements of relative motion and bearings to features. The relative motion sensor measures displacement from one time to the next through some means such as inertial measurement or odometry, as opposed to externally referenced position measurements like compass or GPS. The bearing sensor measures the direction toward features from the robot through a sensor such as an omnidirectional camera, as opposed to bearing and range sensors such as laser rangefinders, sonar, or millimeter wave radar. ::: A full solution to the bearing-only SLAM problem must take into consideration detecting and identifying features and estimating the location of the features as well as the motion of the robot using the measurements. This thesis focuses on the estimation problem given that feature detection and data association are available. Estimation requires a solution that is fast, accurate, consistent, and robust. ::: In an applied sense, this dissertation puts forth a methodology for building maps and localizing a mobile robot using odometry and monocular vision. This sensor suite is chosen for its simplicity and generality, and in some sense represents a minimal configuration for localization and mapping. ::: In a broader sense, the dissertation describes a novel method for state estimation applicable to problems which exhibit particular nonlinearity and sparseness properties. The method relies on deterministic sampling in order to compute sufficient statistics at each time step in a recursive filter. The relationship of the new algorithm to bundle adjustment and Kalman filtering (including some of its variants) is discussed. <s> BIB003 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Basic Principles and Specific Characteristics <s> Recent advances in CCD technologies, processing speed and image understanding have been driving the development of the camera-based positioning systems. An improved performance of optical systems has triggered image based positioning methods to become an attractive alternative for applications in industrial metrology as well as for robot- and pedestrian navigation. This paper provides a survey of current optical indoor positioning approaches. Different systems are briefly described and categorized based on how the images are referenced to the environment. <s> BIB004
As explained earlier, VLC based positioning is the technique of using the VLC signal transmitted from base stations to find the position of a mobile device. Usually LEDs are used as the VLC transmitters while a PD or an image sensor is used for receiving the positioning signal, which might contain the ID or the geographical location of the LEDs, or any and all information useful for the positioning process. Figure 1 describes a typical VLC based indoor positioning system where the LED lamps whose original function is to shed light are now used for transmitting a positioning signal, which only contains the ID of LED lamps. Note that the information about the location of the LED lamps is always required for determining the position of the mobile device. In this case, the mobile device might need to obtain such information from its (pre-installed) database which stores the locations of the LED lamps corresponding to their ID. When a camera is used as the receiver, VLC based positioning might look similar to the vision based positioning BIB004 BIB001 BIB003 BIB002 which has been developed in robot vision or augmented reality applications, for quite long time. The key difference between the two techniques is the manner in which the mobile device obtains positioning information. While in VLC based positioning using a camera, this information is obtained by receiving VLC signals actively transmitted from the LED base station. With the conventional vision based positioning, this information is obtained by processing images of (natural or artificial) landmarks in the positioning environment. Even if LEDs are used as artificial landmarks, vision based positioning still differs from VLC based positioning in that these LEDs only radiate unmodulated light which does not convey any information. Hardware ID 