RPLF-VINS: Robust Point–Line Flow-Enhanced Monocular Visual–Inertial SLAM for Low-Light Environments
Jiaqi Dong, Zuhao Zhang, Yiruo Lin, Zengzeng Lian, Zhenghua Zhang, Hu Liu, Guoliang Chen · IEEE Internet of Things Journal · 2025
Monocular visual-inertial SLAM (VINS) confronts formidable challenges in subterranean environments such as mines, where inadequate illumination and feature-deficient textures substantially degrade system performance. Conventional VINS frameworks and contemporary line-enhanced SLAM methodologies exhibit significant performance deterioration under such conditions. This paper presents RPLF-VINS, a robust monocular VINS framework integrating hybrid point-line flow features, specifically engineered for operation in photometrically challenging underground environments. Our system introduces two principal innovations: a SSR-CLAHE image enhancement pipeline synergizing Single-Scale Retinex (SSR) with Contrast-Limited Adaptive Histogram Equalization (CLAHE) to address non-uniform illumination while preserving critical texture details through global luminance correction and localized contrast optimization; and a novel line-feature residual estimation strategy that combines Euclidean distance with plane-normal-angle metrics to more accurately model 3D reprojection errors of line observations, balancing geometric and angular error contributions. Extensive experimental validation across public benchmarks and custom underground datasets demonstrates our system’s superior performance, with quantitative evaluations revealing 40.0%, 50.0%, and 25.0% RMSE reductions compared to state-of-the-art VINS-Mono, PL-VINS, and EPLF-VINS implementations, respectively. RPLF-VINS therefore offers practical value for underground IoT applications by enabling more reliable navigation and mapping in low-light, low-texture settings.