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

TL;DR

VIPS-Odom is a semantic visual-inertial odometry system that accurately localizes autonomous vehicles in underground parking by integrating parking slot detection with multi-sensor fusion, outperforming traditional methods.

Contribution

The paper introduces a novel tightly-coupled optimization framework that fuses parking slot detection with visual-inertial odometry for improved underground parking localization.

Findings

Outperforms baseline methods in parking scenarios

Robust parking slot tracking with multi-object tracking framework

Effective sensor fusion in challenging underground environments

Abstract

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.