3D object recognition has been a cutting-edge research topic since the popularization of depth cameras. These cameras enhance the perception of the environment and so are particularly suitable for autonomous robot navigation applications. Advanced deep learning approaches for 3D object recognition are based on complex algorithms and demand powerful hardware resources. However, autonomous robots and powered wheelchairs have limited resources, which affects the implementation of these algorithms for real-time performance. We propose to use instead a 3D voxel-based extension of the 2D histogram of oriented gradients (3DVHOG) as a handcrafted object descriptor for 3D object recognition in combination with a pose normalization method for rotational invariance and a supervised object classifier. The experimental goal is to reduce the overall complexity and the system hardware requirements, and thus enable a feasible real-time hardware implementation. This article compares the 3DVHOG object recognition rates with those of other 3D recognition approaches, using the ModelNet10 object data set as a reference. We analyze the recognition accuracy for 3DVHOG using a variety of voxel grid selections, different numbers of neurons (N_h) in the single hidden layer feedforward neural network, and feature dimensionality reduction using principal component analysis. The experimental results show that the 3DVHOG descriptor achieves a recognition accuracy of 84.91% with a total processing time of 21.4 ms. Despite the lower recognition accuracy, this is close to the current state-of-the-art approaches for deep learning while enabling real-time performance.

自深度相机普及以来，3D对象识别一直是最前沿的研究主题。这些摄像机增强了对环境的感知，因此特别适合于自主机器人导航应用。用于3D对象识别的高级深度学习方法基于复杂的算法，并且需要强大的硬件资源。但是，自动机器人和电动轮椅的资源有限，这会影响这些算法的实时性能。我们建议改为使用基于3D体素的定向梯度2D直方图扩展（3DVHOG）作为用于3D对象识别的手工对象描述符，结合用于旋转不变性的姿势归一化方法和受监督的对象分类器。实验目标是降低总体复杂度和系统硬件要求，从而实现可行的实时硬件实现。本文使用ModelNet10对象数据集作为参考，将3DVHOG对象识别率与其他3D识别方法进行了比较。我们使用多种体素网格选择，不同数量的神经元来分析3DVHOG的识别准确性（N _h）在单隐藏层前馈神经网络中，并使用主成分分析来降低特征维数。实验结果表明，3DVHOG描述符的识别精度为84.91％，总处理时间为21.4 ms。尽管识别精度较低，但它与当前用于深度学习的最新方法非常接近，同时可以实现实时性能。