Key points are not available for this paper at this time.
This paper presents a novel method for simultaneous localization and mapping (SLAM), specifically designed to address the unique challenges of unmanned vehicle localization in environments where GNSS signals are unavailable. This novel approach integrates two types of ranging beacons: vehicle-mounted and dynamically deployable. Initially, the paper introduces a technique for estimating the relative position and orientation of multiple vehicles, utilizing the signal characteristics of the specifically arranged vehicle-mounted beacons. Subsequently, our approach incorporates dynamically deployable beacons to identify previously visited areas. This integration facilitates closed-loop corrections, effectively reducing map drift, while accomplishing these tasks with minimal computational resources. In the final stage, our methodology proves its effectiveness in highly degraded scenarios. We successfully achieve localization and correct map construction using dynamic beacons, even in challenging environments. The experimental results validate the capability of our approach to meet the positioning requirements of multiple unmanned vehicles navigating GNSS-denied terrains. Additionally, the approach contributes to the generation of a more precise point cloud map, further enhancing the mapping accuracy in these complex operational settings.
Zhang et al. (Tue,) studied this question.