Due to object recognition accuracy limitations, unmanned ground vehicles (UGVs) must perceive their environments for local path planning and object avoidance. To gather high-precision information about the UGV’s surroundings, Light Detection and Ranging (LiDAR) is frequently used to collect large-scale point clouds. However, the complex spatial features of these clouds, such as being unstructured, diffuse, and disordered, make it difficult to segment and recognize individual objects. This paper therefore develops an object feature extraction and classification system that uses LiDAR point clouds to classify 3D objects in urban environments. After eliminating the ground points via a height threshold method, this describes the 3D objects in terms of their geometrical features, namely their volume, density, and eigenvalues. A back-propagation neural network (BPNN) model is trained (over the course of many iterations) to use these extracted features to classify objects into five types. During the training period, the parameters in each layer of the BPNN model are continually changed and modified via back-propagation using a non-linear sigmoid function. In the system, the object segmentation process supports obstacle detection for autonomous driving, and the object recognition method provides an environment perception function for terrain modeling. Our experimental results indicate that the object recognition accuracy achieve 91.5% in outdoor environment.

中文翻译：

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使用反向传播神经网络对LiDAR点云中的3D对象进行分类

由于对象识别精度的限制，无人地面车辆（UGV）必须感知其环境以进行局部路径规划和避免对象。为了收集有关UGV周围环境的高精度信息，经常使用光检测和测距（LiDAR）来收集大规模点云。但是，这些云的复杂空间特征（如无结构，弥散和无序）使分割和识别单个对象变得困难。因此，本文开发了一种使用LiDAR点云对城市环境中的3D对象进行分类的对象特征提取和分类系统。通过高度阈值方法消除了地面点后，这将根据3D对象的几何特征（即它们的体积，密度和特征值）来对其进行描述。训练了反向传播神经网络（BPNN）模型（在许多迭代过程中），以使用这些提取的特征将对象分为五种类型。在训练期间，使用非线性S型函数通过反向传播不断更改和修改BPNN模型每一层中的参数。在该系统中，对象分割过程支持自动驾驶的障碍物检测，并且对象识别方法为地形建模提供了环境感知功能。我们的实验结果表明，在室外环境下物体识别的准确率达到91.5％。BPNN模型的每一层中的参数通过使用非线性S型函数的反向传播进行连续更改和修改。在该系统中，对象分割过程支持自动驾驶的障碍物检测，并且对象识别方法为地形建模提供了环境感知功能。我们的实验结果表明，在室外环境下物体识别的准确率达到91.5％。BPNN模型的每一层中的参数通过使用非线性S型函数的反向传播进行连续更改和修改。在该系统中，对象分割过程支持自动驾驶的障碍物检测，并且对象识别方法为地形建模提供了环境感知功能。我们的实验结果表明，在室外环境下物体识别的准确率达到91.5％。