College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Noblelift Intelligent Equipment Co., Ltd., Huzhou 313100, China.
Sensors (Basel). 2023 Jan 20;23(3):1217. doi: 10.3390/s23031217.
Pallet pose estimation is one of the key technologies for automated fork pickup of driverless industrial trucks. Due to the complex working environment and the enormous amount of data, the existing pose estimation approaches cannot meet the working requirements of intelligent logistics equipment in terms of high accuracy and real time. A point cloud data-driven pallet pose estimation method using an active binocular vision sensor is proposed, which consists of point cloud preprocessing, Adaptive Gaussian Weight-based Fast Point Feature Histogram extraction and point cloud registration. The proposed method overcomes the shortcomings of traditional pose estimation methods, such as poor robustness, time consumption and low accuracy, and realizes the efficient and accurate estimation of pallet pose for driverless industrial trucks. Compared with traditional Fast Point Feature Histogram and Signature of Histogram of Orientation, the experimental results show that the proposed approach is superior to the above two methods, improving the accuracy by over 35% and reducing the feature extraction time by over 30%, thereby verifying the effectiveness and superiority of the proposed method.
托盘位姿估计是无人驾驶工业车辆自动叉取的关键技术之一。由于复杂的工作环境和大量的数据，现有的位姿估计方法在高精度和实时性方面无法满足智能物流设备的工作要求。提出了一种基于主动双目视觉传感器的点云数据驱动的托盘位姿估计方法，该方法由点云预处理、基于自适应高斯权重的快速点特征直方图提取和点云配准三部分组成。所提出的方法克服了传统位姿估计方法的鲁棒性差、耗时和精度低等缺点，实现了无人驾驶工业车辆托盘位姿的高效、准确估计。与传统的快速点特征直方图和方向直方图签名相比，实验结果表明，该方法优于上述两种方法，精度提高了 35%以上，特征提取时间减少了 30%以上，验证了所提方法的有效性和优越性。
