A Robust RGB-D SLAM System for Indoor Environments with Reflective Ground

Visual Simultaneous Localization and Mapping (VSLAM) is a critical technology for intelligent mobile robots, enabling simultaneous environmental mapping and self-localization. However, reflective ground in indoor environments presents significant challenges to VSLAM systems, as it introduces noisy virtual landmarks that degrade both map accuracy and localization precision. To address these challenges, we propose a novel RGB-D VSLAM system specifically designed for these environments. Our approach begins with a carefully designed point-line-plane-based VSLAM framework, which mitigates the limitations caused by sparse point features in indoor settings. Then we analyze how reflective ground degrades VSLAM performance and propose a reflective ground feature removal strategy that integrates semantic and geometric information. To enhance robustness in highly reflective environments, we incorporate a monocular depth estimation network as a complementary module. Extensive experiments on public datasets demonstrate that our framework achieves state-of-the-art performance. And evaluations on an author-collected dataset highlight the system’s superior mapping and localization capabilities in reflective indoor environments. Furthermore, ablation studies validate the effectiveness of each proposed component, and latency tests confirm the system’s suitability for real-time applications.