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.

翻译：本研究探索将人工智能应用于移动机器人领域，实现载货架自主检测与位姿估计以完成自动化拾取任务。设计了一种深度神经网络，用于从RGBD数据中识别载货架上预定义的标志点，进而计算载货架的位姿。该网络直接处理RGBD图像以估计标志点位置，为确定载货架空间位置奠定基础。通过涵盖软硬件实现的综合实验验证了所提方法。提出了一种基于深度学习的框架，用于检测载货架并估计其位姿，以支持自主物流车辆的应用。该方法采用卷积神经网络从RGBD输入中识别载货架上的特征参考点，并通过将推断出的标志点与先验几何知识相结合来计算其位姿。实验结果表明，所获精度足以在工业环境中实现可靠的载货架检测，证实了该方法在自主内部物流应用中的适用性。