CornerVINS: Accurate Localization and Layout Mapping for Structural Environments Leveraging Hierarchical Geometric Representations

Abstract

A compact and consistent map of surroundings is critical for intelligent robots to understand their situations and realize robust navigation. Most existing techniques rely on infinite planes, which are sensitive to pose drift and may lead to confusing maps. Toward high-level perception in indoor environments, we propose CornerVINS, an innovative RGB-D inertial localization and layout mapping method leveraging hierarchical geometric features, i.e., points, planes, and box corners. Specifically, points are enhanced by fusing depth information, and planes are modeled as bounded patches using convex hulls to increase their discriminability. More importantly, box corners, lying at the intersection of three orthogonal planes, are parameterized with a 6-D vector and integrated into the extended Kalman filter for the first time. We introduce a hierarchical mechanism to effectively extract and associate planes and corners, which are considered as layout components of scenes and serve as long-term landmarks to correct camera poses. Extensive experiments prove that the proposed box corners bring significant improvements, enabling accurate localization and consistent layout mapping at low computational cost. Overall, the proposed CornerVINS outperforms state-of-the-art systems in both accuracy and efficiency.