| Abstract |
The widespread deployment of Unmanned Autonomous Systems (UAS) across smart cities has intensified the demand for reliable and high-precision indoor positioning solutions. While Wi-Fi Round-Trip-Time (RTT) technology offers promising absolute positioning capabilities and LiDAR-inertial odometry (LIO) provides accurate relative positioning, both face inherent limitations - RTT suffers from signal interference and processing delays, while LIO accumulates drift over time. Although existing methods have explored loose coupling of these technologies, they process sensor data independently, failing to fully exploit their complementary strengths. This paper presents a novel tightly-coupled Wi-Fi RTT/LiDAR/Inertial integration framework using factor graph optimization for robust indoor positioning. Our approach implements comprehensive multi-sensor fusion at the raw measurement level, incorporating scan-to-multiscan LiDAR factors and innovative RTT range factors within a unified optimization framework. The system employs a two-stage optimization process with progressive iteration schemes to effectively handle both RTT measurement outliers and LIO drift accumulation. Through extensive experimental evaluations in both controlled indoor and challenging outdoor environments, our proposed framework demonstrates superior performance by achieving root mean square errors (RMSE) of 0.76 meters and 2.09 meters respectively, representing substantial improvements over both RTT measurement least square result (reductions of 63.5% and 29.2%) and traditional LiDAR-inertial odometry (improvements of 22.4% and 54.1%), while maintaining consistent accuracy across different operational scenarios. © 2025 IEEE. |
