6458 IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 6, NO. 4, OCTOBER 2021
Patchwork: Concentric Zone-Based Region-Wise
Ground Segmentation With Ground Likelihood
Estimation Using a 3D LiDAR Sensor
Hyungtae Lim , Student Member, IEEE, Minho Oh, and Hyun Myung , Senior Member, IEEE
Abstract—Ground segmentation is crucial for terrestrial mobile
platforms to perform navigation or neighboring object recognition.
Unfortunately, the ground is not flat, as it features steep slopes;
bumpy roads; or objects, such as curbs, flower beds, and so forth.
To tackle the problem, this letter presents a novel ground segmen-
tation method called Patchwork, which is robust for addressing the
under-segmentation problem and operates at more than 40 Hz.
In this letter, a point cloud is encoded into a Concentric Zone
Model–based representation to assign an appropriate density of
cloud points among bins in a way that is not computationally com-
plex. This is followed by Region-wise Ground Plane Fitting, which
is performed to estimate the partial ground for each bin. Finally,
Ground Likelihood Estimation is introduced to dramatically re-
duce false positives. As experimentally verified on SemanticKITTI
and rough terrain datasets, our proposed method yields promising
performance compared with the state-of-the-art methods, showing
faster speed compared with existing plane fitting–based methods.
Code is available: https://github.com/LimHyungTae/patchwork
Index Terms—Range sensing, mapping, field robots, ground
segmentation.
I. INTRODUCTION
I
N RECENT years, there has been an increased demand to
perceive surroundings for mobile platforms, such as Un-
manned Ground Vehicles (UGVs), Unmanned Aerial Vehicles
(UAVs), or autonomous cars. To accomplish this, numerous
researchers have applied various 3D perception methods [1]–[4].
In particular, a 3D light detection and ranging (LiDAR) sensor
has been extensively deployed due to allowing for centimeter-
level accuracy and omnidirectional sensing, as well as its ability
Manuscript received February 24, 2021; accepted June 10, 2021. Date of
publication June 28, 2021; date of current version July 15, 2021. This letter
was recommended for publication by Associate Editor J. Behley and Editor S.
Behnke upon evaluation of the reviewers’ comments. This work was supported
by the Industry Core Technology Development Project 20005062, Development
of Artificial Intelligence Robot Autonomous Navigation Technology for Agile
Movement in Crowded Space, funded by the Ministry of Trade, Industry, and
Energy (MOTIE, Republic of Korea), and by the research project “Development
of A.I. based recognition, judgement, and control solution for autonomous vehi-
cle corresponding to atypical driving environment,” which is financed from the
Ministry of Science, and ICT (Republic of Korea) Contract No. 2019-0-00399.
The students are supported by the BK21 FOUR from the Ministry of Education
(Republic of Korea). (Corresponding author: Hyun Myung.)
The authors are with the School of Electrical Engineering, KI-AI, KI-
R at KAIST (Korea Advanced Institute of Science, and Technology), Dae-
jeon 34141, South Korea (e-mail: shapelim@kaist.ac.kr; minho.oh@kaist.ac.kr;
hmyung@kaist.ac.kr).
This letter has supplementary downloadable material available at https://doi.
org/10.1109/LRA.2021.3093009, provided by the authors.
Digital Object Identifier 10.1109/LRA.2021.3093009
Fig. 1. Overview of ourproposed method called Patchwork. Patchwork mainly
consists of three parts: Concentric Zone Model (CZM)–based polar grid repre-
sentation, Region-wise Ground Plane Fitting (R-GPF), and Ground Likelihood
Estimation (GLE).
to measure great distances compared with stereo cameras [1],
[5], [6]. Accordingly, a 3D point cloud captured by a LiDAR
sensor is utilized for semantic segmentation [7], [8], tracking [9],
detection [10], and so forth.
In this letter, we specifically focus on ground segmentation
tasks [11], [12]. There are two main purposes of ground segmen-
tation. One is to estimate the movable area [3],[13] for successful
navigation. The other purpose, on which this l etter places more
emphasis, i s the segmentation of a point cloud to recognize or
track moving objects. Terrestrial vehicles or humans inevitably
come into contact with the ground [14]; ideally, dynamic objects
can be recognized in a simple way, such as through Euclidean
clustering if the ground is well estimated [8], [15]. Furthermore,
because most cloud points belong to the ground, ground segmen-
tation can significantly reduce computational power when one is
performing object segmentation or detection in a preprocessing
stage [16]. Thus, ground in this study refers to not only the road,
which is a movable area, but also all regions that moving objects
can come into contact with, including sidewalks or lawns.
In this study, as presented in Fig. 1, we propose a novel
Concentric Zone Model (CZM)–based region-wise ground seg-
mentation method, called Patchwork, which is an extension of
Region-wise Ground Plane Fitting (R-GPF) in our previous
study [14]. The aim of R-GPF in our previous study was to
estimate the ground points for static map building purposes,
whereas here, we focus only on ground segmentation on a 3D
point cloud. We also conduct detailed experiments on the impact
of the bin size, which was not covered in our previous paper.
In summary, t he contribution of this letter is threefold:
r
To the best of our knowledge, it is the first attempt to ana-
lyze the impact of bin size when estimating ground planes
in complex urban environments using the SemanticKITTI
dataset [1]. Accordingly, an efficient, non-uniform,
region-wise representation of a 3D point cloud is proposed,
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文章的内容:
提出Patch
work分割方
法,结果是
能在超过40
Hz的情况下
稳健工作
总结研究结果,表明性能更好,可以给后人提供参考(价值)。
指出了研究背景/问题:地面分割是目标识别的关键
,而路缘、花草等存在造成了干扰
简要介绍研究方法、过程:提出同心
区域模型、计算点云密度、地面拟合
目前,地面分割相对常用的是圆形区域的点云拟合,这个题目指出了文章的方向
——点云分割,以及创新点:同心区域
研究价值:智能驾驶、3D感知发展趋势下,
地面分割是识别和跟踪移动对象的前提。