论文标题
拼布++：使用3D点云快速且稳健的地面分割解决部分不足
Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud
论文作者
论文摘要
在使用3D激光雷达传感器的3D感知领域中，地面分割是各种目的的必不可少的任务，例如可穿越的区域检测和对象识别。在这种情况下，已经提出了几种地面分割方法。但是，仍然遇到一些限制。首先，某些地面分割方法需要对参数进行微调，具体取决于周围环境，这过于费力且耗时。此外，即使参数进行了充分的调整，部分分割问题仍然可能出现，这意味着某些地区的地面细分失败。最后，当地面在另一个结构（例如固定壁）之上时，地面分割方法通常无法估计适当的接地平面。为了解决这些问题，我们提出了一种称为Patchwork ++的强大的地面分割方法，该方法是拼布的扩展。 Patchwork ++利用自适应地面可能性估计（A-GLE），以根据先前的地面分割结果自适应地计算适当的参数。此外，暂时的地面还原（TGR）通过使用临时地面财产来减轻部分不足的细分问题。同样，即使用不同的层抬高地面，也会引入区域垂直平面拟合（R-VPF），以正确分割接地平面。最后，我们提出反射的噪声去除（RNR），以根据3D激光雷达反射模型有效地消除虚拟噪声点。我们使用Semantickitti数据集证明了定性和定量评估。我们的代码可从https://github.com/url-kaist/patchwork-plusplus获得
In the field of 3D perception using 3D LiDAR sensors, ground segmentation is an essential task for various purposes, such as traversable area detection and object recognition. Under these circumstances, several ground segmentation methods have been proposed. However, some limitations are still encountered. First, some ground segmentation methods require fine-tuning of parameters depending on the surroundings, which is excessively laborious and time-consuming. Moreover, even if the parameters are well adjusted, a partial under-segmentation problem can still emerge, which implies ground segmentation failures in some regions. Finally, ground segmentation methods typically fail to estimate an appropriate ground plane when the ground is above another structure, such as a retaining wall. To address these problems, we propose a robust ground segmentation method called Patchwork++, an extension of Patchwork. Patchwork++ exploits adaptive ground likelihood estimation (A-GLE) to calculate appropriate parameters adaptively based on the previous ground segmentation results. Moreover, temporal ground revert (TGR) alleviates a partial under-segmentation problem by using the temporary ground property. Also, region-wise vertical plane fitting (R-VPF) is introduced to segment the ground plane properly even if the ground is elevated with different layers. Finally, we present reflected noise removal (RNR) to eliminate virtual noise points efficiently based on the 3D LiDAR reflection model. We demonstrate the qualitative and quantitative evaluations using a SemanticKITTI dataset. Our code is available at https://github.com/url-kaist/patchwork-plusplus
