LeGO-LOAM_LightweightandGround-OptimizedLidarOdometryandMapping

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2019/9/23 通天塔 LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

http://tongtianta.site/paper/8117 1/24

通天塔 (/)

LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and

Mapping on Variable Terrain

LeGO-LOAM：轻型和地面优化的激光雷达测距和可变地形测绘

论文：http://static.tongtianta.site/paper_pdf/1f7896c2-08b7-11e9-b8e4-00163e08bb86.pdf

(http://static.tongtianta.site/paper_pdf/1f7896c2-08b7-11e9-b8e4-00163e08bb86.pdf)

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Tixiao Shan and Brendan Englot

Abstract— We propose a lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six

degree-of-freedom pose estimation with ground vehicles. LeGO-LOAM is lightweight, as it can achieve realtime pose estimation

on a low-power embedded system. LeGOLOAM is ground-optimized, as it leverages the presence of a ground plane in its

segmentation and optimization steps. We ﬁrst apply point cloud segmentation to ﬁlter out noise, and feature extraction to obtain

distinctive planar and edge features. A two-step Levenberg-Marquardt optimization method then uses the planar and edge

features to solve different components of the six degree-of-freedom transformation across consecutive scans. We compare the

performance of LeGO-LOAM with a state-of-the-art method, LOAM, using datasets gathered from variable-terrain environments

with ground vehicles, and show that LeGO-LOAM achieves similar or better accuracy with reduced computational expense. We

also integrate LeGO-LOAM into a SLAM framework to eliminate the pose estimation error caused by drift, which is tested using

the KITTI dataset.

摘要 - 我们提出了一种轻量级和地面优化的激光雷达测距和测绘方法LeGO-LOAM，用于与地面车辆进行实时六自由度

姿态估计。LeGO-LOAM重量轻，因为它可以在低功耗嵌入式系统上实现实时姿态估计。LeGOLOAM经过地面优化，因

为它在分割和优化步骤中利用了地平面的存在。我们首先应用点云分割来滤除噪声，并进行特征提取以获得独特的平面

和边缘特征。然后，两步Levenberg-Marquardt优化方法使用平面和边缘特征来解决连续扫描中六自由度变换的不同分

量。我们使用从地形车辆的变地形环境收集的数据集，将LeGO-LOAM的性能与最先进的方法LOAM进行比较，并表明

LeGO-LOAM在降低计算成本的同时实现了类似或更好的准确性。我们还将LeGO-LOAM集成到SLAM框架中，以消除由

漂移引起的姿态估计误差，该误差使用KITTI数据集进行测试。

I. INTRODUCTION

一，导言

Among the capabilities of an intelligent robot, mapbuilding and state estimation are among the most fundamental prerequisites.

Great efforts have been devoted to achieving real-time 6 degree-of-freedom simultaneous localization and mapping (SLAM) with

vision-based and lidar-based methods. Although vision-based methods have advantages in loop-closure detection, their sensitivity

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2019/9/23 通天塔 LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

http://tongtianta.site/paper/8117 2/24

to illumination and viewpoint change may make such capabilities unreliable if used as the sole navigation sensor. On the other

hand, lidarbased methods will function even at night, and the high resolution of many 3D lidars permits the capture of the ﬁne

details of an environment at long ranges, over a wide aperture. Therefore, this paper focuses on using 3D lidar to support real-

time state estimation and mapping.

在智能机器人的功能中，地图构建和状态估计是最基本的先决条件。我们致力于通过基于视觉和基于激光雷达的方法实

现实时6自由度同时定位和制图（SLAM）。虽然基于视觉的方法在闭环检测中具有优势，但是如果用作唯一的导航传感

器，它们对照明和视点变化的敏感性可能使这些能力不可靠。另一方面，基于激光雷达的方法即使在夜晚也能发挥作

用，并且许多3D激光雷达的高分辨率允许在很宽的光圈范围内捕捉远距离环境的细节。因此，本文重点介绍如何使用

3D激光雷达来支持实时状态估计和映射。

The typical approach for ﬁnding the transformation between two lidar scans is iterative closest point (ICP) [1]. By ﬁnding

correspondences at a point-wise level, ICP aligns two sets of points iteratively until stopping criteria are satisﬁed. When the

scans include large quantities of points, ICP may suffer from prohibitive computational cost. Many variants of ICP have been

proposed to improve its efﬁciency and accuracy [2]. [3] introduces a point-to-plane ICP variant that matches points to local

planar patches. Generalized-ICP [4] proposes a method that matches local planar patches from both scans. In addition, several

ICP variants have leveraged parallel computing for improved efﬁciency [5]–[8].

在两个激光雷达扫描之间进行转换的典型方法是迭代最近点（ICP）[1]。通过在逐点水平上找到对应关系，ICP迭代地

对齐两组点，直到满足停止标准。当扫描包括大量点时，ICP可能遭受过高的计算成本。已经提出许多ICP的变体来提高

其效率和准确度[2]。 [3]介绍了一种点到平面的ICP变体，它将点与局部平面斑块相匹配。Generalized-ICP [4]提出了一

种匹配两次扫描的局部平面贴片的方法。此外，一些ICP变体利用并行计算来提高效率[5] - [8]。

T. Shan and B. Englot are with the Department of Mechanical Engineering, Stevens Institute of Technology, Castle Point on

Hudson, Hoboken NJ 07030 USA, .

T. Shan和B. Englot是机械工程系，史蒂文斯理工学院，Castle Point on Hudson，Hoboken NJ 07030 USA，

。

Feature-based matching methods are attracting more attention, as they require less computational resources by extracting

representative features from the environment. These features should be suitable for effective matching and invariant of point-of-

view. Many detectors, such as Point Feature Histograms (PFH) [9] and Viewpoint Feature Histograms (VFH) [10], have been

proposed for extracting such features from point clouds using simple and efﬁcient techniques. A method for extracting general-

purpose features from point clouds using a Kanade-Tomasi corner detector is introduced in [11]. A framework for extracting line

and plane features from dense point clouds is discussed in [12].

基于特征的匹配方法正在吸引更多关注，因为它们通过从环境中提取代表性特征而需要较少的计算资源。这些特征应该

适用于有效匹配和视点不变。已经提出了许多检测器，例如点特征直方图（PFH）[9]和视点特征直方图（VFH）[10]，

用于使用简单有效的技术从点云中提取这些特征。在[11]中介绍了使用Kanade-Tomasi角点检测器从点云中提取通用特征

的方法。在[12]中讨论了从密集点云中提取线和平面特征的框架。

Many algorithms that use features for point cloud registration have also been proposed. [13] and [14] present a keypoint

selection algorithm that performs point curvature calculations in a local cluster. The selected keypoints are then used to perform

matching and place recognition. By projecting a point cloud onto a range image and analyzing the second derivative of the depth

values, [15] selects features from points that have high curvature for matching and place recognition. Assuming the environment

is composed of planes, a plane-based registration algorithm is proposed in [16]. An outdoor environment, e.g., a forest, may limit

the application of such a method. A collar line segments (CLS) method, which is especially designed for Velodyne lidar, is

presented in [17]. CLS randomly generates lines using points from two consecutive “rings” of a scan. Thus two line clouds are

generated and used for registration. However, this method suffers from challenges arising from the random generation of lines. A

2019/9/23 通天塔 LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

http://tongtianta.site/paper/8117 3/24

segmentation-based registration algorithm is proposed in [18]. SegMatch ﬁrst applies segmentation to a point cloud. Then a

feature vector is calculated for each segment based on its eigenvalues and shape histograms. A random forest is used to match

the segments from two scans. Though this method can be used for online pose estimation, it can only provide localization

updates at about 1Hz.

还提出了许多使用点云注册功能的算法。 [13]和[14]提出了一种关键点选择算法，该算法在本地集群中执行点曲率计

算。然后使用所选关键点来执行匹配和位置识别。通过将点云投影到距离图像上并分析深度值的二阶导数，[15]从具有

高曲率的点中选择特征以进行匹配和位置识别。假设环境由平面组成，在[16]中提出了基于平面的配准算法。室外环境

（例如森林）可能限制这种方法的应用。在[17]中提出了一种专为Velodyne激光雷达设计的领线段（CLS）方法。CLS使

用来自扫描的两个连续“环”的点随机生成线。因此，生成两个线云并用于注册。然而，该方法遭受由随机生成线产生的

挑战。在[18]中提出了基于分割的配准算法。SegMatch首先将分段应用于点云。然后基于其特征值和形状直方图为每个

片段计算特征向量。随机森林用于匹配来自两次扫描的片段。虽然这种方法可以用于在线姿态估计，但它只能提供大约

1Hz的定位更新。

A low-drift and real-time lidar odometry and mapping (LOAM) method is proposed in [19] and [20]. LOAM performs point

feature to edge/plane scan-matching to ﬁnd correspondences between scans. Features are extracted by calculating the roughness

of a point in its local region. The points with high roughness values are selected as edge features. Similarly, the points with low

roughness values are designated planar features. Real-time performance is achieved by novelly dividing the estimation problem

across two individual algorithms. One algorithm runs at high frequency and estimates sensor velocity at low accuracy. The other

algorithm runs at low frequency but returns high accuracy motion estimation. The two estimates are fused together to produce a

single motion estimate at both high frequency and high accuracy. LOAM’s resulting accuracy is the best achieved by a lidar-only

estimation method on the KITTI odometry benchmark site [21].

在[19]和[20]中提出了一种低漂移和实时激光雷达测距和测绘（LOAM）方法。LOAM对边缘/平面扫描匹配执行点特征以

匹配扫描之间的对应关系。通过计算其局部区域中的点的粗糙度来提取特征。选择具有高粗糙度值的点作为边缘特征。

类似地，具有低粗糙度值的点被指定为平面特征。通过在两个单独的算法之间新颖地划分估计问题来实现实时性能。一

种算法以高频率运行并以低精度估计传感器速度。另一种算法以低频运行但返回高精度运动估计。将两个估计值融合在

一起以产生高频率和高精度的单个运动估计。LOAM的最终精确度是KITTI测距基准站点上仅限激光雷达的估算方法所

能达到的最佳效果[21]。

In this work, we pursue reliable, real-time six degreeof-freedom pose estimation for ground vehicles equipped with 3D lidar, in a

manner that is amenable to efﬁcient implementation on a small-scale embedded system. Such a task is non-trivial for several

reasons. Many unmanned ground vehicles (UGVs) do not have suspensions or powerful computational units due to their limited

size. Non-smooth motion is frequently encountered by small UGVs driving on variable terrain, and as a result, the acquired data

is often distorted. Reliable feature correspondences are also hard to ﬁnd between two consecutive scans due to large motions

with limited overlap. Besides that, the large quantities of points received from a 3D lidar poses a challenge to real-time processing

using limited on-board computational resources. When we implement LOAM for such tasks, we can obtain low-drift motion

estimation when a UGV is operated with smooth motion admist stable features, and supported by sufﬁcient computational

resources. However, the performance of LOAM deteriorates when resources are limited. Due to the need to compute the

roughness of every point in a dense 3D point cloud, the update frequency of feature extraction on a lightweight embedded

system cannot always keep up with the sensor update frequency. Operation of UGVs in noisy environments also poses

challenges for LOAM. Since the mounting position of a lidar is often close to the ground on a small UGV, sensor noise from the

ground may be a constant presence. For example, range returns from grass may result in high roughness values. As a

consequence, unreliable edge features may be extracted from these points. Similarly, edge or planar features may also be

2019/9/23 通天塔 LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

http://tongtianta.site/paper/8117 4/24

extracted from points returned from tree leaves. Such features are usually not reliable for scan-matching, as the same grass blade

or leaf may not be seen in two consecutive scans. Using these features may lead to inaccurate registration and large drift.

在这项工作中，我们为配备3D激光雷达的地面车辆追求可靠，实时的六自由度姿态估计，其方式适合在小规模嵌入式系

统上有效实施。由于几个原因，这样的任务非常重要。由于尺寸有限，许多无人驾驶地面车辆（UGV）没有悬架或强大

的计算单元。在不同地形上行驶的小型UGV经常遇到非平滑运动，因此，所获取的数据经常失真。由于重叠有限的大运

动，在两次连续扫描之间也很难找到可靠的特征对应关系。除此之外，从3D激光雷达接收的大量点对使用有限的机载计

算资源的实时处理提出了挑战。当我们为这些任务实现LOAM时，我们可以获得低漂移运动估计，当UGV以平滑运动提

供稳定特征运行时，并且由足够的计算资源支持。然而，当资源有限时，LOAM的性能会恶化。由于需要计算密集3D点

云中每个点的粗糙度，因此轻量级嵌入式系统上的特征提取更新频率无法始终跟上传感器更新频率。在嘈杂环境中操作

UGV也对LOAM提出了挑战。由于激光雷达的安装位置通常在小UGV上接近地面，因此来自地面的传感器噪声可能是恒

定的存在。例如，草的范围返回可能导致高粗糙度值。结果，可以从这些点提取不可靠的边缘特征。类似地，也可以从

树叶返回的点提取边缘或平面特征。这些特征对于扫描匹配通常是不可靠的，因为在两次连续扫描中可能看不到相同的

草叶或叶片。使用这些功能可能会导致注册不准确和大漂移。

We therefore propose a lightweight and ground-optimized LOAM (LeGO-LOAM) for pose estimation of UGVs in complex

environments with variable terrain. LeGO-LOAM is lightweight, as real-time pose estimation and mapping can be achieved on an

embedded system. Point cloud segmentation is performed to discard points that may represent unreliable features after ground

separation. LeGO-LOAM is also ground-optimized, as we introduce a two-step optimization for pose estimation. Planar features

extracted from the ground are used to obtain during the ﬁrst step. In the second step, the rest of the

transformation is obtained by matching edge features extracted from the segmented point cloud. We also

integrate the ability to perform loop closures to correct motion estimation drift. The rest of the paper is organized as follows.

Section II introduces the hardware used for experiments. Section III describes the proposed method in detail. Section IV

presents a set of experiments over a variety of outdoor environments.

因此，我们提出了一种轻量级和地面优化的LOAM（LeGO-LOAM），用于在具有可变地形的复杂环境中对UGV进行姿

态估计。LeGO-LOAM是轻量级的，因为可以在嵌入式系统上实现实时姿态估计和映射。执行点云分割以丢弃在地面分

离之后可能表示不可靠特征的点。LeGO-LOAM也经过地面优化，因为我们引入了两步优化姿势估计。从地面提取的平

面特征用于在第一步中获得。在第二步中，通过匹配从分段点云提取的边缘特征来获得转换

的其余部分。我们还集成了执行循环闭合以校正运动估计漂移的能力。本文的其余部分安排如下。第二

节介绍了用于实验的硬件。第III节详细描述了所提出的方法。第四部分介绍了各种户外环境的一系列实验。

Fig. 1: Hardware and system overview of LeGO-LOAM.

图1：LeGO-LOAM的硬件和系统概述。

II. SYSTEM HARDWARE

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