## 1 Introduction  
As quality-of-life indices have notably risen, the sophistication and refinement of services have increased accordingly. This trend has created new challenges for service robots [1], requiring continuous advancements to meet the evolving needs of various industries. Navigation and positioning, essential for service robots to perform tasks, have gradually become key areas of focus in this research field [2]. Cutting-edge navigation and positioning methods have emerged as a central focus, drawing considerable interest from both academics and industry professionals. Advancements in this domain are expected to significantly enhance the efficiency and reliability of service robots across different sectors. Therefore, ongoing research into innovative approaches for addressing the complex challenges of navigation and positioning is crucial. Numerous navigation technologies and data fusion methods have been developed to provide continuous and stable navigation for service robots. The widespread adoption of Global Navigation Satellite Systems (GNSSs) is evident across industries like transportation, agriculture, telecommunications, and disaster response. This growth has been fueled by advancements in satellite technology, the widespread availability of GNSS devices, and the seamless integration of GNSS data into various systems and software. For instance, in [3], a two-stage image processing scheme based on the target's translational motion was explored using GNSS-based passive radar. This method proved effective in tracking three freighters illuminated by multiple satellites. The third-generation Beidou system (BDS-3) uses an extended Kalman filter (EKF) to estimate satellite orbits and timing parameters, employing a dual set of standalone filters for seamless real-time functionality [4]. In [5], the authors examined the contribution of the small Tianjin University-1 (TJU-1) satellite, which uses the global positioning system (GPS) for real-time navigation, alongside BDS-2, GLOASS, and BDS-3, for the first time. While many practical applications based on GNSSs have been proposed, GNSS methods provide stable solutions in outdoor environments. However, GNSS positioning accuracy significantly decreases in certain enclosed environments due to signal interference. Various attempts have been made to address this issue. For example, in [6], inertial-assisted, unmodulated visible-light positioning was used to provide stable localization in environments with weak GNSS signals, demonstrating the method's efficacy. In [7], radio frequency identification (RFID) technology was proposed for navigation. In this study, a radio frequency fingerprint was developed, with the support of a Kalman filter, to assist localization. A portable, user-controlled device was used to estimate the distance and angle to the target, guiding the user to the intended RFID-tagged target [8]. In [9], a novel UHF RFID tag localization method based on phase measurements across a synthetic aperture was introduced, generating a holographic image to illustrate the spatial probability density function of the tag's actual position. In [10], ultrawide-band (UWB) technology was used to determine robot coordinates, with a test conducted in a challenging indoor environment. A novel method for non-line-of-sight (NLOS) identification and mitigation using a multi-input parallel deep learning model and a Grampian angular field (GAF) was proposed in [11]. In [12], a novel static person (SP) localization (SPL) method was introduced, featuring an innovative SP detection scheme and integration with SP tracking, offering low computational complexity. However, it is worth noting that these methods require a base station, increasing overall system cost. There are many different approaches to solve localization in indoor environments, e.g., visual SLAM, LiDAR SLAM, etc.