Point cloud data offer the potential for viewpoint-independent object recognition based solely on the geometrical information about an object that they contain. We consider two types of one-dimensional data products extracted from point clouds: range histograms and point-separation histograms. We evaluate each histogram in terms of its viewpoint independence. The Jensen-Shannon divergence is used to show that point-separation histograms have the potential for viewpoint independence. We demonstrate viewpoint-independent recognition performance using lidar data sets from two vehicles and a simple algorithm for a two-class recognition problem. We find that point-separation histograms have good potential for viewpoint-independent recognition over a hemisphere.

中文翻译：

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使用降维点云数据的视点独立对象识别

点云数据提供了仅基于它们包含的对象的几何信息进行独立于视点的对象识别的潜力。我们考虑从点云中提取的两种一维数据产品：范围直方图和点分离直方图。我们根据其视点独立性来评估每个直方图。Jensen-Shannon 散度用于表明点分离直方图具有视点独立性的潜力。我们使用来自两辆车的激光雷达数据集和用于两类识别问题的简单算法来展示视点独立识别性能。我们发现点分离直方图具有在半球上进行独立于视点的识别的良好潜力。