Wearable devices, such as smartwatches, have been widely used by the public for health monitoring, exercise tracking, and message alerts. Regarding localization applications, although smartwatches have been explored by many researchers, current solutions typically require combining data from multiple sensors or multiple mobile devices for reliable position estimation, which is inconvenient for practical usage. In this paper, we propose a novel indoor localization method that relies solely on an inertial measurement unit (IMU) of a smartwatch. First, we design a smartwatch-based neural inertial odometry (WNIO) that adapts to multiple motion patterns and achieves low-drift dead reckoning. Second, we detect interactions between the arm and the environment, such as opening a door, to formulate absolute position measurements that help constrain error drift. A multi-hypothesis Kalman filter (MHKF) is employed to ensure reliable matching and localization. We conducted real-world experiments to validate the effectiveness of the proposed method. The results demonstrate that our method achieves reliable dead reckoning across various motion patterns, including walking and running at different speeds. Furthermore, the method effectively mitigates error drift without relying on any external signals. The video (BiliBili) is also shared to display our research.

The use of mobile devices for indoor localization has proven to be a convenient solution for pedestrians in Internet of Things (IoTs) applications. Radiofrequency (RF) signals, including Wi-Fi, Bluetooth, and others, are among the most commonly used sources. However, their availability cannot be guaranteed in all scenarios. Although pedestrian dead reckoning (PDR) using an inertial measurement unit (IMU) provides a self-contained positioning solution, it is susceptible to error accumulation due to heading uncertainties and varying motions. This article presents a low-drift 3-D deep-inertial odometry (DIO) method for indoor pedestrian localization using an IMU. The proposed approach employs a neural network to regress speeds within the human body frame, ensuring that the speeds are unaffected by absolute heading. These regressed speeds are integrated with inertial navigation to determine position. To enhance accuracy, the method incorporates an invariant extended Kalman filter (InEKF)-based integration for state estimation. Additionally, a learned height is included in the filter to improve 3-D position estimation. The performance of the proposed method is validated through real-world tests in various environments. Results demonstrate that the proposed method outperforms traditional PDR, robust neural inertial navigation (RONIN), and EKF-based techniques. Furthermore, this article examines the method from multiple perspectives, highlighting its strengths in addressing heading drift and varying motions, as well as the impact of height constraints and behavior-based position corrections. The video (YouTube) or (BiliBili) is shared to showcase our work.

Round-Trip Time (RTT) -based Wi-Fi indoor positioning technology is a critical component of the Internet of Things (IoT), enabling various location-based services in smart buildings. However, the positioning accuracy of RTT will be significantly degraded in environments with severe non-line-of-sight (NLOS) reception and a limited number of access points (APs). To solve this challenge, we propose an indoor positioning method based on visibility matching. Compared to the RTT, which is easily affected by NLOS reception, AP visibility is a new positioning feature determined by RTT and signal strength. Additional constraints can be imposed on the positioning system based on the consistency between the observed visibility and the simulated visibility. To improve the accuracy of RTT ranging, a multi-wall ranging model is adopted that considers the influence of signal penetration through walls. The proposed positioning method combines the refined RTT ranging result and visibility constraint. It mitigates the impact of NLOS receptions on positioning accuracy through the multi-wall ranging model and makes full use of the characteristics of NLOS signal through visibility matching. In different experiments, the proposed method improves median positioning accuracy from 2.3 meters to 0.64 meters compared to other methods.

Pedestrian location tracking in emergency responses and environmental surveys of indoor scenarios tend to rely only on their own mobile devices, reducing the usage of external services. Low-cost and small-sized inertial measurement units (IMU) have been widely distributed in mobile devices. However, they suffer from high-level noises, leading to drift in position estimation over time. In this work, we present a graph-based indoor 3D pedestrian location tracking with inertial-only perception. The proposed method uses onboard inertial sensors in mobile devices alone for pedestrian state estimation in a simultaneous localization and mapping (SLAM) mode. It starts with a deep vertical odometry-aided 3D pedestrian dead reckoning (PDR) to predict the position in 3D space. Environment-induced behaviors, such as corner-turning and stair-taking, are regarded as landmarks. Multi-hypothesis loop closures are formed using statistical methods to handle ambiguous data association. A factor graph optimization fuses 3D PDR and behavior loop closures for state estimation. Experiments in different scenarios are performed using a smartphone to evaluate the performance of the proposed method, which can achieve better location tracking than current learning-based and filtering-based methods. Moreover, the proposed method is also discussed in different aspects, including the accuracy of offline optimization and proposed height regression, and the reliability of the multi-hypothesis behavior loop closures. The video (YouTube) or (BiliBili) is also shared to display our research.

In this paper, a new UAV dataset is presented to support UAV research, such as high-precision positioning and dynamic calibration. The presented dataset is divided into two categories based on different research needs. The first category of the dataset contains visual, inertial, and motor encoder information collected in the indoor motion capture room. This dataset provides accurate ground truth generated by motion capture, which is suitable for the study of UAV dynamics model. The other category of the dataset is collected in a variety of complex outdoor scenarios, and the multi-sensor fusion localization algorithm is used to generate high-precision ground truth trajectory, this category of the dataset could be used for research in UAV positioning and scene reconstruction in complex environments. In short, a total of nine sequences of the dataset are provided. More importantly, the timestamps of raw measurements in each sequence are well synchronized and accurately calibrated. The dataset also provides accurate extrinsic and intrinsic parameters and ground truth trajectories.

To autonomous systems (ASs) equipped with single-frequency GNSS chipsets, it is hard to fully use the high-precision carrier phase to assist positioning as the inherent integer ambiguity cannot be reliably obtained using only single-frequency observations, especially in obstructed environments. Although the time difference can be done to eliminate the integer ambiguity to form accurate constraint in graph based fusion, cycle slip is not properly handled in current methods, leading to degraded performance. In this paper, a performance enhancement of tightly coupled GNSS/IMU integration based on factor graph with robust time-differenced carrier phase (TDCP) loop closure is proposed. Different from traditional methods, cycle slip detection is firstly performed, and the effect of cycle slip can be eliminated in the formation of loop closure, which can bring about better robustness. Doppler frequency shift is used to detect cycle slip and carrier phase measurements at corresponding epochs are then selected to build TDCP loop closure. The proposed robust TDCP loop closure is fused with pseudorange, Doppler frequency shift and IMU via factor graph, which can make the full use of the absolute and relative measurements to enhance the performance of tightly coupled GNSS/IMU integration. Simulation and field tests are conducted to validate the proposed method. The results show that the proposed method can improve the positioning performance with robust TDCP loop closure. Meanwhile, the computational load of proposed method is discussed.

Indoor localization by smartphones has indicated its promising application prospect in daily life. Smartphone-based pedestrian dead reckoning (PDR) is a common method to obtain the locations. However, PDR suffers from position error accumulation. Although radio frequency (RF) and indoor map can be utilized to restrain the error drift, it requires the prior deployment of facilities or information, which is unsuitable for unknown environments. This paper proposes a factor graph optimization (FGO)-based indoor pedestrian simultaneous localization and mapping (SLAM) with probabilistic exact activity loop closures using a smartphone. In this paper, the smartphone built-in inertial measurement unit (IMU) is solely used to achieve SLAM, in which the human turning activity is regarded as the landmark. Repeatedly observed activities are then used to form loop closures to restrain the drift. FGO is first utilized to formulate pedestrian IMU-only SLAM, which achieves better estimation accuracy than the filter-based method. Moreover, multi-hypothesis tracking is employed to deal with ambiguous data association. During the turning, key points are defined and mutually matched to form exact loop closures to improve estimation accuracy. Simulations and experimental tests are both done to evaluate the performance of the proposed method.

Modelling error of smartphone-reported Global Navigation Satellite System (GNSS) locations plays an important role in urban navigation under mixed LOS/NLOS environments. In the case of pedestrian navigation, the performance of GNSS error modeling significantly affects the precision of final multi-source fusion. In this work, a novel Long Short-Term Memory (LSTM) network is developed for error modeling of smartphone-reported GNSS locations combined with the detected human motion information. The LSTM network is applied to adaptively combine multi-level observations provided by GNSS and built-in sensors-based location sources under a specific time period instead of considering only adjacent timestamps. The motion features extracted from multi-level observations is then modeled as the input vector of LSTM for training and prediction purposes, and the predicted errors under two axis in the n-frame are finally modeled as the error covariance matrix and applied in the multi-sources fusion structure. The comprehensive experiments indicate the effectivity and significant improvement for integrated localization after GNSS error modeling.