駐車枠と密結合した視覚慣性オドメトリによる自動駐車
VIPS-Odom: Visual-Inertial Odometry Tightly-coupled with Parking Slots for Autonomous Parking
駐車枠検出と特徴点を統合し、IMU・車輪速・駐車枠の制約を密結合最適化で融合する地下駐車場向け視覚慣性オドメトリを提案。
著者: Xuefeng Jiang, Fangyuan Wang, Rongzhang Zheng, Han Liu, Yixiong Huo, Jinzhang Peng, Lu Tian, Emad Barsoum
分類: cs.RO
原文アブストラクト
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.
