Vision-Guided Object Recognition and 6D Pose Estimation System Based on Deep Neural Network for Unmanned Aerial Vehicles towards Intelligent Logistics

Abstract

Unmanned aerial vehicle (UAV) express delivery is facing a period of rapid development and continues to promote the aviation logistics industry due to its advantages of elevated delivery efficiency and low labor costs. Automatic detection, localization, and estimation of 6D poses of targets in dynamic environments are key prerequisites for UAV intelligent logistics. In this study, we proposed a novel vision system based on deep neural networks to locate targets and estimate their 6D pose parameters from 2D color images and 3D point clouds captured by an RGB-D sensor mounted on a UAV. The workflow of this system can be summarized as follows: detect the targets and locate them, separate the object region from the background using a segmentation network, and estimate the 6D pose parameters from a regression network. The proposed system provides a solid foundation for various complex operations for UAVs. To better verify the performance of the proposed system, we built a small dataset called SIAT comprising some household staff. Comparative experiments with several state-of-the-art networks on the YCB-Video dataset and SIAT dataset verified the effectiveness, robustness, and superior performance of the proposed method, indicating its promising applications in UAV-based delivery tasks.