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《激光感知：基于同心圆区的区域化地面分割——Patchwork方法》在现代移动平台，如无人地面车辆(UGVs)、无人机(UAVs)和自动驾驶汽车中，对周围环境的感知需求日益增长。为了实现这一目标，众多研究者已经采用各种三维感知方法。其中，三维激光雷达(LiDAR)传感器因其厘米级的精度、全方位的感知能力以及与立体相机相比更远的测量距离而被广泛使用。然而，地面分割是这些平台进行导航或邻近物体识别的关键，而现有的地面分割算法在处理不平整地面时面临挑战，例如陡峭的坡度、凹凸不平的道路以及路缘石、花坛等障碍物。针对这些问题，本文提出了一种名为Patchwork的新颖地面分割方法，它对下分割问题有较强的鲁棒性，并且运行频率超过40Hz。Patchwork方法的核心在于将点云编码为基于同心圆区模型的表示，以在各个区间内分配适当密度的云点，同时保持较低的计算复杂度。随后进行区域化地面平面拟合，以估计每个区间的局部地面。引入地面概率估计，显著减少误检。通过在SemanticKITTI和粗糙地形数据集上的实验验证，提出的Patchwork方法相比于最先进的方法展现出颇具潜力的性能，而且在速度上优于现有的平面拟合方法。具体来说，文章首先介绍了一种将点云编码到同心圆区模型的方法。这种方法将空间划分为多个同心圆环，每个环代表一个特定的距离范围。通过这种方式，可以更好地捕捉地面的不规则形状，同时降低了计算资源的需求。接着，区域化地面平面拟合阶段，每个圆环内的点被用来独立估计局部地面平面，这样能够处理复杂的地形情况，如坡地和小凸起。这种方法比全局平面拟合更能精确地捕捉地面的变化。然后，地面概率估计是解决误检问题的关键。通过对每个点的地面概率进行估计，可以更准确地区分地面点和其他非地面点，从而减少错误分类。这一步骤结合了之前步骤的成果，提高了分割结果的准确性。实验部分，文章对比了Patchwork方法在SemanticKITTI和粗糙地形数据集上的表现，证明了其在处理复杂环境下的优越性能。此外，代码的开源性（可在https://github.com/LimHyungTae/patchwork获取）使得其他研究者和开发者能够进一步测试和改进这个方法。 Patchwork方法通过创新的点云处理策略和地面分割技术，提升了移动平台在不平整地面上的导航和物体识别能力。这种方法不仅在性能上有所突破，而且在实时性方面也具有优势，为未来智能系统的地面感知提供了一条新的研究路径。

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Patchwork: Concentric Zone-based Region-wise

Ground Segmentation with Ground Likelihood

Estimation Using a 3D LiDAR Sensor

Hyungtae Lim

, Student Member, IEEE, Minho Oh

, Hyun Myung

, Senior Member, IEEE

Abstract—Ground segmentation is crucial for terrestrial mo-

bile 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 paper presents a novel

ground segmentation method called Patchwork, which is robust

for addressing the under-segmentation problem and operates

at more than 40 Hz. In this paper, 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 complex. 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 reduce false positives. As experimen-

tally 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

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 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 paper, we speciﬁcally focus on ground segmentation

tasks [11], [12]. There are two main purposes of ground

segmentation. One is to estimate the movable area [3], [13] for

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 & Energy (MOTIE, Republic of Korea) and

by the research project “Development of A.I. based recognition, judgement

and control solution for autonomous vehicle 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).

Hyungtae Lim,

Minho Oh, and

Hyun Myung are with the School of

Electrical Engineering, KI-AI, KI-R at KAIST (Korea Advanced Institute

of Science and Technology), Daejeon, 34141, South Korea. {shapelim,

minho.oh, hmyung}@kaist.ac.kr

GLE

Concentric Zone Model Output of GLE

Ground

Estimate

3D scan

For each bin

R-GPF

Fig. 1. Overview of our proposed method called Patchwork.

Patchwork mainly consists of three parts: Concentric Zone

Model (CZM)–based polar grid representation, Region-wise

Ground Plane Fitting (R-GPF), and Ground Likelihood Esti-

mation (GLE).

successful navigation. The other purpose, on which this paper

places more emphasis, is 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 segmentation 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

segmentation 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, the contribution of this paper is threefold:

• To the best of our knowledge, it is the ﬁrst attempt to

analyze the impact of bin size when estimating ground

planes in complex urban environments using the Se-

manticKITTI dataset [1]. Accordingly, an efﬁcient, non-

uniform, region-wise representation of a 3D point cloud

is proposed, referred to as a CZM–based representation

whose bin size is different depending on each zone.

• Also, we leverage Ground Likelihood Estimation (GLE)

in terms of uprightness, elevation, and ﬂatness to deter-

arXiv:2108.05560v2 [cs.RO] 10 Mar 2022

mine whether each bin is ground.

• Our proposed method shows promising performance over

the state-of-the-art, region-wise ﬁtting–based methods at

more than 40 Hz. In particular, Patchwork estimates

the ground points with the least recall variance, which

shows that our proposed method overcomes the under-

segmentation issue in complex urban environments.

II. RELATED WORKS

A. The Difﬁculties of Ground Segmentation

One may argue that it is a simple task that can be easily esti-

mated by ﬁltering a point cloud based on sensor height or using

RANSAC [17] which is a renowned method for estimating a

plane. Unfortunately, there are three main issues that impede

algorithms from conducting precise ground segmentation: a)

there exists a partially steep slope or bumpy road, b) curbs

or ﬂower beds make some regions uneven, and c) because all

surrounding objects are taken into account as outliers in the

ground segmentation tasks, these objects hinder plane ﬁtting.

For these reasons, sometimes under-segmentation occurs, in

which case points belonging to different objects are merged

into the same segment [6], [12].

B. Ground Plane Estimation Methods

To tackle these issues, numerous researchers have studied

various approaches. For instance, Douillard et al. [4] and Chen

et al. [18] employed Gaussian process–based methods. On the

other hand, Tse et al. [19], Byun et al. [3], and Rummelhard

et al. [20] proposed Markov Random Field–based methods.

These methods can be used to estimate detailed ground points

yet requiring much computational time, so it may not be

appropriate to use them as preprocessing algorithms whose

speed should be guaranteed at more than 20 Hz.

C. Scan Representation

Meanwhile, grid representation–based methods have been

widely utilized to leverage expressibility compared with sin-

gular plane model–based methods [4], [9]. In particular, polar

grid representation, which treats a point cloud in cylindrical

coordinates, is commonly employed these days because it

naturally compensates for the geometric characteristics of

3D LiDAR sensors [11], [12], [14], [16], [21]. In practice,

Thrun et al. [5] presented a grid cell–based binary ground

classiﬁcation method in a probabilistic way to predict the

movable area for autonomous driving in the DARPA challenge.

These methods are mainly divided into two categories: a)

elevation map–based and b) model ﬁtting–based methods.

Accordingly, the latter category can be further classiﬁed into

two main methodologies: a) line ﬁtting–based and b) plane

ﬁtting–based methods.

D. Elevation Map–based 2.5D Grid Representation

First, elevation map–based methods are used to distinguish

between ground and non-ground points by encoding a 3D

point cloud into 2.5D grid representations [5], [9]. Thrun et al.

[5] utilized relative height and Asvadi et al. [9] used average

height and its covariance on each grid. These methods have

strong advantages over other methods in terms of speed and

computational cost. However, there are some potential risks

that sometimes a steep slope region could be considered as a

non-ground region because of large z value difference between

its supremum and inﬁmum points with respect to Z-axis.

E. Multiple Line Fitting–based Ground Segmentation

Next, Himmelsbach et al. [11] and Steinhauser et al. [21]

introduced 2D line ﬁtting on a uniform polar grid repre-

sentation to estimate the straight-line equation on each grid.

Then, in each grid, it was determined whether points were

ground points by comparing between constant thresholds and

the estimated parameters, such as the point-to-line distance,

gradient, or y-intercept.

F. Multiple Plane Fitting–based Ground Segmentation

Sharing their views of region-wise ﬁtting yet improving ro-

bustness, other researchers have conducted region-wise plane

ﬁtting–based approaches [8], [12], [14], [16]. For instance,

Zermas et al. [8] divided a point cloud into three parts

along the x-axis of the body frame, which is the forward

direction of a vehicle. This method is based on the premise

that a slope usually changes along the x-axis; however, this

assumption sometimes fails when it comes to a bumpy road

or a complex intersection. To resolve the problem, Narksri

et al. [12] proposed a slope-robust method using consecutive

ring patterns in the scan data as well as the concept of the

continuity of the region-wise estimated plane along the radial

direction. Furthermore, Narksri et al. [12] and Cheng et al.

[16] proposed an adaptive way of setting a grid size depending

on the density of the cloud points or the incidence angle.

G. Deep Learning-based Methods

Of course, as the deep learning era has come, Milioto et

al. [7] proposed RangeNet++ to estimate point-wise labels

on a 3D point cloud and Paigwar et al. [22] presented

GndNet, which estimates ground plane elevation information

in a grid–based representation to discern ground points in

real time. Unfortunately, these methods usually require high

computational resources. In addition, these methods tend to

be highly ﬁtted to the environments of train dataset; thus, the

performance of those can be potentially degraded when used

in quite different environments from the training dataset or

different sensor conﬁguration [23].

III. METHODOLOGY OF PATCHWORK

The following paragraphs highlight the problem deﬁnition

and the reasoning behind each module of Patchwork. Patch-

work mainly consists of three parts: CZM, R-GPF, and GLE.

A. Problem Deﬁnition

First, we begin by denoting a point cloud at the moment

as P. Then, let P = {p

, p

, . . . , p

} be a set of

cloud points that contain N points at the moment acquired by

a 3D LiDAR sensor, where each point p

consists of p

, y

, z

} in the Cartesian coordinates. In this paper, P is

deﬁnitely classiﬁed into two classes: a set of ground points,

G, and its complement, G

, which satisfy G ∪ G

= P. Note

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