The pursuit of reliable and high-precision indoor positioning has become increasingly critical with the widespread deployment of Unmanned Autonomous Systems (UAS) across smart cities. While Wi-Fi Round-Trip-Time (RTT) technology offers promising absolute positioning capabilities, it faces challenges from signal interference and processing delays. Similarly, LiDAR-inertial odometry (LIO) systems provide accurate relative positioning, but suffer from cumulative drift over time. Although existing methods have explored loosely coupled technologies, they process sensor data separately, failing to fully exploit the complementary strengths of different sensors. This research pioneered a tightly-coupled RTT/LIO framework, encompassing novel factor graph formulations that ensure consistency between RTT and LiDAR observations, alongside LiDAR-aided RTT outlier detection and exclusion. Furthermore, we developed an innovative approach to estimate the positions of unknown access points (AP) by using prior trajectory and RTT observations. AP position estimation is based on kernel density estimation (KDE) and geometric diversity constraints (GDC) with the help of an adaptive RANSAC-based fault detection algorithm. Compared to RTT-only implementations, state-of-the-art LIO systems, and conventional loosely coupled approaches, our method demonstrated error reductions of 20-80% in extensive experiments. The implementation of our proposed methodology has been made publicly available on GitHub. The video Bilibili is also shared to display our research.

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.

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.