AING-SLAM: Accurate Implicit Neural Geometry-Aware SLAM With Appearance and Semantics via History-Guided Optimization

Abstract

In range-based SLAM systems, localization accuracy depends on the quality of geometric maps. Sparse LiDAR scans and noisy depth from RGB-D sensors often yield incomplete or inaccurate reconstructions that degrade pose estimation. Appearance and semantic cues, readily available from onboard RGB and pretrained models, can serve as complementary signals to strengthen geometry. Nevertheless, variations in appearance due to illumination or texture and inconsistencies in semantic labels across frames can hinder geometric optimization if directly used as supervision. To address these challenges, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AING-SLAM</b>, an Accurate Implicit Neural Geometry-aware SLAM framework that allows appearance and semantics to effectively strengthen geometry in both mapping and odometry. A unified neural point representation with a lightweight cross-modal decoder integrates geometry, appearance and semantics, enabling auxiliary cues to refine geometry even in sparse or ambiguous regions. For pose tracking, appearance-semantic-aided odometry jointly minimizes SDF, appearance, and semantic residuals with adaptive weighting, improving scan-to-map alignment and reducing drift. To safeguard stability, a history-guided gradient fusion strategy aligns instantaneous updates with long-term optimization trends, mitigating occasional inconsistencies between appearance/semantic cues and SDF-based supervision, thereby strengthening geometric optimization. Extensive experiments on indoor RGB-D and outdoor LiDAR benchmarks demonstrate real-time performance, state-of-the-art localization accuracy, and high-fidelity reconstruction across diverse environments.