A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> SLAM. <s> The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on an exact factorization of the posterior into a product of conditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far beyond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and real-world data. <s> BIB001 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> SLAM. <s> WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization. <s> BIB002 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> SLAM. <s> Being able to build a map of the environment and to simultaneously localize within this map is an essential skill for mobile robots navigating in unknown environments in absence of external referencing systems such as GPS. This so-called simultaneous localization and mapping (SLAM) problem has been one of the most popular research topics in mobile robotics for the last two decades and efficient approaches for solving this task have been proposed. One intuitive way of formulating SLAM is to use a graph whose nodes correspond to the poses of the robot at different points in time and whose edges represent constraints between the poses. The latter are obtained from observations of the environment or from movement actions carried out by the robot. Once such a graph is constructed, the map can be computed by finding the spatial configuration of the nodes that is mostly consistent with the measurements modeled by the edges. In this paper, we provide an introductory description to the graph-based SLAM problem. Furthermore, we discuss a state-of-the-art solution that is based on least-squares error minimization and exploits the structure of the SLAM problems during optimization. The goal of this tutorial is to enable the reader to implement the proposed methods from scratch. <s> BIB003 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> SLAM. <s> We present a Simultaneous Localization and Mapping (SLAM) algorithm based on measurements of the ambient magnetic field strength (MagSLAM) that allows quasi-real-time mapping and localization in buildings, where pedestrians with foot-mounted sensors are the subjects to be localized. We assume two components to be present: firstly a source of odometry (human step measurements), and secondly a sensor of the local magnetic field intensity. Our implementation follows the FastSLAM factorization using a particle filter. We augment the hexagonal transition map used in the pre-existing FootSLAM algorithm with local maps of the magnetic field strength, binned in a hierarchical hexagonal structure. We performed extensive experiments in a number of different buildings and present the results for five data sets for which we have ground truth location information. We consider the results obtained using MagSLAM to be strong evidence that scalable and accurate localization is possible without an a priori map. <s> BIB004
In the case that fingerprint database is not available, SLAM can be used for tracking a participant and sensing the signals around the participant meantime. SLAM is a standard mathematical framework for iteratively optimizing (1) the trajectory (sequence of poses) or dynamics of a user based on the prediction of the motion model and observations of the user (the observations could be landmarks, images, range measurements, or radio frequency measurements) and (2) the position of landmark and the 2D/3D map itself. SLAM has been widely applied in robotics. Recently, increasing research induces the SLAM framework into the radio map or magnetic map generation, such as WiFi SLAM BIB002 and MagSLAM BIB004 . Taking the noise of sensor measurements into account, a SLAM problem can be formulated as a probabilistic form. Assuming that a user moving around in an unknown environment with a sequence states of X 1: = { 1 , 2 , . . . , }, the user senses the environment to obtain the perceptions Z 1: = { 1 , 2 , . . . , } and acquire the odometry measurements D 1: = { 1 , 2 , . . . , }. Solving the full SLAM problem needs estimating posterior probability of the user's trajectory X 1: and the map M of the environment given all measurements and an initial state 0 . The posterior probability is denoted as In the crowd sensing based fingerprint generation approach, D 1: can be estimated by PDR via smartphones. M could be represented as fingerprints. 0 is an arbitrary location in the target area. The SLAM schemes such as FastSLAM BIB001 , GraphSLAM BIB003 , GP-LVM SLAM BIB002 , or DPSLAM  could easily be implemented to run in real time on a smartphone.