HPPS: A Hierarchical Progressive Perception System for Luggage Trolley Detection and Localization at Airports

TL;DR

The paper introduces HPPS, a hierarchical perception system that improves detection and localization of luggage trolleys under partial occlusion using RGB images, enhancing autonomous airport luggage collection.

Contribution

The novel HPPS method processes position and orientation separately, requiring only RGB images, and continuously refines trolley pose under occlusion, outperforming existing approaches.

Findings

More reliable detection under partial occlusion

Accurate localization with minimal keypoints

Validated effectiveness in real-world airport scenarios

Abstract

The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys at airports. However, existing methods for detecting and locating these luggage trolleys often fail when they are not fully visible. To address this, we introduce the Hierarchical Progressive Perception System (HPPS), which enhances the detection and localization of luggage trolleys under partial occlusion. The HPPS processes the luggage trolley's position and orientation separately, which requires only RGB images for labeling and training, eliminating the need for 3D coordinates and alignment. The HPPS can accurately determine the position of the luggage trolley with just one well-detected keypoint and estimate the luggage trolley's orientation when it is partially occluded. Once the luggage trolley's initial pose is detected, HPPS updates this information continuously to refine its accuracy until the robot begins grasping. The experiments on detection and localization demonstrate that HPPS is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. A website about this work is available at HPPS.