Tightly-Coupled, Speed-aided Monocular Visual-Inertial Localization in Topological Map
This addresses localization for vehicles by reducing reliance on expensive sensors like GPS and LiDAR, though it is incremental as it builds on existing topological mapping and filtering methods.
The paper tackles vehicle localization by proposing a speed-aided monocular visual-inertial algorithm using a topological map, achieving superior performance in challenging scenarios like tunnels.
This paper proposes a novel algorithm for vehicle speed-aided monocular visual-inertial localization using a topological map. The proposed system aims to address the limitations of existing methods that rely heavily on expensive sensors like GPS and LiDAR by leveraging relatively inexpensive camera-based pose estimation. The topological map is generated offline from LiDAR point clouds and includes depth images, intensity images, and corresponding camera poses. This map is then used for real-time localization through correspondence matching between current camera images and the stored topological images. The system employs an Iterated Error State Kalman Filter (IESKF) for optimized pose estimation, incorporating correspondence among images and vehicle speed measurements to enhance accuracy. Experimental results using both open dataset and our collected data in challenging scenario, such as tunnel, demonstrate the proposed algorithm's superior performance in topological map generation and localization tasks.