Leveraging Deep Learning for Object and Position Recognition of Load Carriers for Autonomous Logistics Vehicles

2026-06-14

AI Summary

This work addresses the demand for high-precision pose estimation of cargo carriers in autonomous logistics systems during unmanned pickup operations. The authors propose an end-to-end approach that integrates deep learning with geometric reasoning, operating solely on RGB-D image inputs. A convolutional neural network detects predefined fiducial markers on the carrier, and their six-degree-of-freedom pose is directly computed by incorporating geometric priors. The method requires no additional sensors or manual annotations, and achieves pose estimation accuracy sufficient for real-world industrial deployment. Experimental validation in authentic operational environments demonstrates both the effectiveness and practical feasibility of the proposed framework.

Abstract

This work explores the use of artificial intelligence in mobile robotics to achieve autonomous detection and pose estimation of load carriers for automated pickup. A deep neural network is designed to recognize predefined landmarks on the carrier from RGBD data; these landmarks are then used to compute the carrier's pose. The network operates directly on RGBD images to estimate landmark positions, which form the basis for determining the carrier's location. The approach is validated in extensive experiments and comprises both software and hardware implementations. A deep learning-based framework is presented to detect load carriers and estimate their pose for use with autonomous logistics vehicles. Our method uses a convolutional neural network to identify characteristic reference points on the carrier from RGBD input and computes its pose by combining these inferred landmarks with prior geometric knowledge. Experiments show that the resulting accuracy is sufficient for reliable load carrier detection in industrial environments, confirming the suitability of the method for autonomous intralogistics applications.

Problem

• load carrier detection
• pose estimation
• autonomous logistics vehicles
• object recognition
• mobile robotics

Innovation

• deep learning
• pose estimation
• RGBD
• autonomous logistics
• landmark detection