This paper presents a novel architecture for point cloud road user detection, which is based on a classical point cloud proposal generator approach, that utilizes simple geometrical rules. New methods are coupled with this technique to achieve extremely small computational requirement, and mAP that is comparable to the state-of-the-art. The idea is to specifically exploit geometrical rules in hopes of faster performance. The typical downsides of this approach, e.g. global context loss, are tackled in this paper, and solutions are presented. This approach allows real-time performance on a single core CPU, which is not the case with end-to-end solutions presented in the state-of-the-art. We have evaluated the performance of the method with the public KITTI dataset, and with our own annotated dataset collected with a small mobile robot platform. Moreover, we also present a novel ground segmentation method, which is evaluated with the public SemanticKITTI dataset.
翻译:本文介绍了基于经典的云源建议生成器方法的点云路用户探测新结构,该结构以经典云源建议生成器方法为基础,采用简单的几何规则。新方法与这一技术相结合,以实现极小的计算要求和与最新工艺相近的 mAP 。其想法是具体利用几何规则,希望更快的性能。本文将探讨这一方法的典型下方,例如全球背景损失,并提出解决方案。这一方法允许在单一核心CPU上实时性能,而最新技术提供的端到端解决方案则不是这样。我们用公众的KITTI数据集和我们自己的通过小型移动机器人平台收集的附加注释数据集对这种方法的性能进行了评估。此外,我们还提出了一种与公共Semictic KITTI数据集评价的新型地面分割方法。