  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

Robotics;Computer Vision and Pattern Recognition