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

翻译：在使用 3D LiDAR 传感器的 3D 感知 3D 感知 领域,地面分解对于各种目的来说是一项基本任务,例如跨区域探测和物体识别。 在这种情况下,提出了几种地面分解方法。 但是,仍然遇到一些限制。 首先,有些地面分解方法需要根据周围环境对参数进行细调,因为周围环境是过度劳累和耗时的。此外,即使参数调整良好,部分分解问题仍可能出现,这意味着某些地区的地面分解失败。最后,地面分解方法通常无法估计适当的地面平面,地面高于另一个结构,例如保留墙。为了解决这些问题,我们提出了一种称为补丁++的强势地面分解方法。补法+利用适应性地面可能性估计(A-GLEE),根据先前的地域分解结果来计算适当的参数。此外,时间差分解模型(TGR) 利用临时地面财产来缓解部分的分解问题。同样,区域-垂直平面平面(R-ARPF) 也用我们目前的平面平面标准 3 向地面平面显示我们目前的平面的平面数据。