  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. We develop a multi-object
tracking framework to robustly track parking slots' states. To prove the
superiority of our method, we equip an electronic vehicle with related sensors
and build an experimental platform based on ROS2 system. Extensive experiments
demonstrate the efficacy and advantages of our method compared with other
baselines for parking scenarios.
