A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Zee <s> The FeetSLAM technique builds on iterative processing of multiple sets of pedestrian odometry data, based on FootSLAM. The objective is to obtain maps of large areas based on many data sets. The central idea is that maps originating from other data sets are used as a so-called prior map for a given data set. We show that this follows from the optimal FeetSLAM derivation but is more suited to practical computation limitations such as limited memory. It also yields maps which are not overly dominated by one data set but rather balances the characteristics of each with the effect of averaging out errors. Over iterations, FootSLAM maps are gradually combined to yield a high-accuracy global map the iteration speed is controlled by employing concepts from simulated annealing. We validate our approach using two data sets from two locations, consisting of four and five walks respectively. <s> BIB001 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Zee <s> Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1600m2, and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey. <s> BIB002 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Zee <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> BIB003 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Zee <s> The large number of applications that rely on indoor positioning encourages more advancement in this field. Smartphones are becoming a common presence in our daily life, so taking advantage of their sensors can help to provide ubiquitous positioning solution. We propose HiMLoc, a novel solution that synergistically uses Pedestrian Dead Reckoning (PDR) and WiFi fingerprinting to exploit their positive aspects and limit the impact of their negative aspects. Specifically, HiMLoc combines location tracking and activity recognition using inertial sensors on mobile devices with location-specific weighted assistance from a crowd-sourced WiFi fingerprinting system via a particle filter. By using just the most common sensors available on the large majority of smartphones (accelerometer, compass, and WiFi card) and offering an easily deployable method (requiring just the locations of stairs, elevators, corners and entrances), HiMLoc is shown to achieve median accuracies lower than 3 meters in most cases. <s> BIB004
The following users work almost the same way as previous one except that their initial position distribution is narrowed down to a smaller region thanks to the Wi-Fi fingerprint contributed by prior walks. An augmented particle filter is applied during the Wi-Fi crowdsourcing phase and then the deterministic or probabilistic positioning algorithms can use the Zee-based crowdsourcing fingerprint database. Performance is evaluated by conducting experiments in a 35 m by 65 m office building. The result shows that 50% of localization error is less than 1.2 m and that 80% is less than 2.3 m which is lower than that of pure probabilistic positioning approach, but the site survey efforts are significantly reduced. BIB002 . The authors of LiFS propose a novel framework for fingerprint-based indoor localization, utilizing MDS (multidimensional scaling) twice to map scanned RSS signals to the path that a participant traversed. Unlike previous SLAM based solutions, LiFS only measures walking steps between fingerprints, thus avoiding dealing with long-term drift of dead reckoning. The first-time MDS is used is to map the sample locations in real floor plan into a stressfree floor plan in which the Euclidean distance between two positions reflects the walking distance of the corresponding positions in real floor plan. Then, MDS is applied again to generate the fingerprint space. Reference points like corridors and doors are recognized in fingerprint space and are mapped to their locations in the stress-free floor map. Eventually, all fingerprints can be associated with their corresponding locations by performing a linear transformation. The localization experiment using RADAR-like algorithm yields a result of 5.88 m average localization error and 10.91% room error rate in a 1600 m 2 experiment environment. BIB003 . MagSLAM is a variation on SLAM (Simultaneous Localization and Mapping), which incorporates ambient magnetic field signal. In this framework, the magnetic environment map which is generated from magnetic field measurement is incorporated to build a Dynamic Bayesian Network (DBN) model that is extended from FootSLAM BIB001 which utilizes pure odometry data. Also, the authors extend the spatially binned map in FootSLAM to a hierarchical way with different sized hexagonal cells to achieve an effective map representation. On top of that, a simple Monte Carlo approximation is applied to the results generated from the Bayesian estimator. They presented the results of 5 experiments with ground truth datasets, comparing the performance under different settings of map layers and SLAM algorithm used. Their result shows that MagSLAM can achieve a localization accuracy of 9 cm to 22 cm, which greatly exceeds the performance of using given magnetic map in the same environment. BIB004 . HiMLoc is a hybrid framework that combines pedestrian dead reckoning (PDR), Wi-Fi fingerprinting, and activity recognition to address crowdsourced indoor positioning. It also uses a particle filter to integrate the location estimation of activity classifier, PDR, Map Knowledge, and Wi-Fi positioning components. The Wi-Fi fingerprint database is then updated with the Wi-Fi observation and its corresponding location annotation. The performance of this framework is evaluated in different scenarios, single floor, multiple floors, and a new environment during deployment. In most cases of the first two scenarios, HiMLoc reports a median accuracy of less than 3 m. When applied to new environment, the performance of HiMLoc improves over time due to the fast accuracy convergence, which enables it to be easily deployed in new environment.