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The accuracy of existing ultra-wideband (UWB) range-based indoor localization methods is generally degraded due to the non-line-of-sight (NLOS) situations where a serve bias in UWB range measurements is unavoidable. In this article, we first propose a two-stage NLOS detection method to detect line-of-sight (LOS)-measured distances in mixed LOS/NLOS indoor environments. Then, a high-accuracy UWB/IMU/Odometer integrated localization system is presented using an adaptive multi-algorithm localization framework based on the number of detected LOS-measured distances. Specifically, under conditions of one or two LOS-measured distances, an improved adaptive EKF positioning algorithm (IAEKF) is proposed. Compared with the traditional extended Kalman filter (EKF)-based fusion scheme, the weight function of innovation is exploited to adaptively estimate the measurement noise covariance matrices and further reduce the influence of the changing measurement noise in NLOS conditions. For three or more LOS-measured ranges, a novel tightly coupled fusion factor graph framework is developed. To further improve the performance of seamless positioning in transaction areas, a strong constraint of trajectory smoothness is designed and added to the factor graph framework using the weight value of IMU/odometer measurements. The experimental results show that the proposed localization system achieves an average localization error of 0.227 m, which surmounts UWB range-based and integrated methods in LOS/NLOS mixed environments.

Currently, Wi-Fi-based indoor localization methods have been proven to be promising due to their low deployment cost. However, the overhead of constructing and maintaining maps remains a bottleneck for the widespread deployment of Wi-Fi-based indoor localization methods. In this article, we propose a novel combined Wi-Fi and vision to construct and maintain maps. This method consists of three parts (including constructing the logarithmic distance path loss (LDPL) model using an improved whale optimization algorithm (IWOA), a novel fusion localization module (called LDPL-PF), and a lightweight threshold-based map maintenance model). Specifically, the LDPL model based on IWOA can first construct high-quality maps with limited data. Then, LDPL-PF localization method is used to determine the user’s location based on the map. Finally, a feedback mechanism is introduced to achieve map maintenance automatically. The localization results and the collected data are fed into a multidecision mechanism to build a feedback network for long-term map maintenance. Extensive experimental results show that our proposed method has good accuracy and stability with state-of-the-art methods.

Currently, most mobile devices have WIFI and camera modules to locate their position. However, there are two main challenges in large, highly similar indoor environments (localization accuracy and localization time). Aiming to balance these problems, we propose a sequential-multi-decision integrated system that combines WIFI and vision to acquire users’ locations. This system has two phases: sequential fusion localization and adaptive multi-decision fusion localization. The former employs WIFI-based localization first, then image-based localization and fusion localization are used within the constraints of WIFI-based localization. In the WIFI-based localization phase, the gaussian process regression (GPR) model is used to construct a WIFI indoor map. Subsequently, we propose to apply the hybrid whale optimization algorithm (HWOA) to WIFI-based localization to improve its accuracy and stability. The latter uses an adaptive multi-decision fusion mechanism that integrates WIFI-based localization, image-based localization, and fusion localization to obtain the users’ location finally. The experiments show the effectiveness of HWOA applied to WIFI-based localization. We also experimentally evaluate the proposed fusion algorithm with other state-of-the-art fusion algorithms (e.g., accuracy and time) in a real environment (an area larger than 10,000$m^{2}$m2). The experimental results show that the proposed fusion system is competitive.

The accuracy of existing Wi-Fi fingerprint-based indoor localization methods with smartphones is generally limited by the perceptual aliasing of Wi-Fi signals and device heterogeneity. In this article, we present a high-accuracy composite fingerprint-based localization method for these problems by combining Wi-Fi, space, and pedestrian dead reckoning (PDR) information in a multiuser scenario. Unlike common Wi-Fi fingerprints, we adopt fast appearance-based mapping (FAB-MAP) schemes for Wi-Fi and space information to improve fingerprint-matching accuracy with negligible computation. To achieve this, we propose a binarization procedure for composite fingerprints combining Wi-Fi mean differential fingerprint (MDF) and turning landmarks. The turning landmarks are marked on a floorplan in the offline stage and subsequently identified using inertial measurement unit (IMU) sensor data in the online stage. We then use the Bayesian-based FAB-MAP to obtain the matched location. Finally, a global pose graph is constructed to estimate the users’ trajectories given the composite fingerprint, PDR, and loop closing constraints. The experiments on real and public datasets demonstrate that our method achieves an average localization error of 1.75 and 1.77 m, respectively, which surmounts the classic Wi-Fi fingerprint-based methods, especially in multiuser scenarios.