Department of Electronics Design, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, Sweden.
System Design Department, IMMS Institut für Mikroelektronik- und Mechatronik-Systeme Gemeinnützige GmbH (IMMS GmbH), Ehrenbergstraße 27, 98693 Ilmenau, Germany.
Sensors (Basel). 2021 Jan 29;21(3):910. doi: 10.3390/s21030910.
3D object recognition is an generic task in robotics and autonomous vehicles. In this paper, we propose a 3D object recognition approach using a 3D extension of the histogram-of-gradients object descriptor with data captured with a depth camera. The presented method makes use of synthetic objects for training the object classifier, and classify real objects captured by the depth camera. The preprocessing methods include operations to achieve rotational invariance as well as to maximize the recognition accuracy while reducing the feature dimensionality at the same time. By studying different preprocessing options, we show challenges that need to be addressed when moving from synthetic to real data. The recognition performance was evaluated with a real dataset captured by a depth camera and the results show a maximum recognition accuracy of 81.5%.
3D 目标识别是机器人和自动驾驶车辆中的一个通用任务。在本文中，我们提出了一种使用基于梯度直方图的 3D 扩展对象描述符的 3D 目标识别方法，该方法使用深度相机获取的数据。所提出的方法利用合成物体进行目标分类器的训练，并对深度相机捕获的真实物体进行分类。预处理方法包括实现旋转不变性的操作，以及在同时降低特征维度的情况下最大化识别精度。通过研究不同的预处理选项，我们展示了当从合成数据转移到真实数据时需要解决的挑战。使用深度相机捕获的真实数据集评估了识别性能，结果表明最大识别准确率为 81.5%。
