ICP-Based Pallet Tracking for Unloading on Inclined Surfaces by Autonomous Forklifts

2026-02-17

AI Summary

This study addresses the dragging issue encountered during pallet unloading by autonomous forklifts on inclined surfaces, which arises from misalignment between the pallet and forks. To resolve this, the authors propose a real-time point cloud registration method based on the Iterative Closest Point (ICP) algorithm to dynamically estimate the relative pose between the pallet and the forks. The system continuously adjusts the fork orientation to maintain parallelism with the inclined surface, enabling smooth extraction along the slope. This work represents the first application of ICP for real-time relative pose tracking during unloading operations, integrating 3D point cloud processing with motion control. The effectiveness of the approach is validated through both dynamic simulations and real-world forklift experiments, demonstrating high-precision, drag-free unloading in a simulated truck bed inclination scenario.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

This paper proposes a control method for autonomous forklifts to unload pallets on inclined surfaces, enabling the fork to be withdrawn without dragging the pallets. The proposed method applies the Iterative Closest Point (ICP) algorithm to point clouds measured from the upper region of the pallet and thereby tracks the relative position and attitude angle difference between the pallet and the fork during the unloading operation in real-time. According to the tracking result, the fork is aligned parallel to the target surface. After the fork is aligned, it is possible to complete the unloading process by withdrawing the fork along the tilt, preventing any dragging of the pallet. The effectiveness of the proposed method is verified through dynamic simulations and experiments using a real forklift that replicate unloading operations onto the inclined bed of a truck.

Problem

• pallet unloading
• inclined surfaces
• autonomous forklifts
• fork alignment
• drag prevention

Innovation

• ICP algorithm
• pallet tracking
• autonomous forklift
• inclined surface unloading
• real-time pose estimation