VIPS-Odom: Visual-Inertial Odometry Tightly-coupled with Parking Slots for Autonomous Parking

Understand

Precise localization is of great importance for autonomous parking task since it provides service for the downstream planning and control modules, which significantly affects the system performance.

• For parking scenarios, dynamic lighting, sparse textures, and the instability of global positioning system (GPS) signals pose challenges for most traditional localization methods.
• To address these difficulties, we propose VIPS-Odom, a novel semantic visual-inertial odometry framework for underground autonomous parking, which adopts tightly-coupled optimization to fuse measurements from multi-modal sensors and solves odometry.
• Our VIPS-Odom integrates parking slots detected from the synthesized bird-eye-view (BEV) image with traditional feature points in the frontend, and conducts tightly-coupled optimization with joint constraints introduced by measurements from the inertial measurement unit, wheel speed sensor and parking slots in the backend.