LeGO-LOAM_ Lightweight and Ground-Optimized Lidar Odometry and M....pdf

发布时间：2022-05-31 发布人：admin 分类：说明书资料大小：12.26M 资料格式：pdf 举报版权申诉

weixin_42784393-12202565-16359647256591157711.pdf-第1页.png

第1页 / 共24页

weixin_42784393-12202565-16359647256591157711.pdf-第2页.png

第2页 / 共24页

weixin_42784393-12202565-16359647256591157711.pdf-第3页.png

第3页 / 共24页

weixin_42784393-12202565-16359647256591157711.pdf-第4页.png

第4页 / 共24页

weixin_42784393-12202565-16359647256591157711.pdf-第5页.png

第5页 / 共24页

weixin_42784393-12202565-16359647256591157711.pdf-第6页.png

第6页 / 共24页

weixin_42784393-12202565-16359647256591157711.pdf-第7页.png

第7页 / 共24页

weixin_42784393-12202565-16359647256591157711.pdf-第8页.png

第8页 / 共24页

文本预览

2019/9/23 通天塔 LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain 通天塔 (/) 作者\标题\内容 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) 报错申请删除 Tixiao Shan and Brendan Englot 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 http://tongtianta.site/paper/8117 1/24 搜索

通天塔 LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain 2019/9/23 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 http://tongtianta.site/paper/8117 2/24

通天塔 LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain 2019/9/23 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 http://tongtianta.site/paper/8117 3/24

通天塔 LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain 2019/9/23 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。 during the ﬁrst step. In the second step, the rest of the is obtained by matching edge features extracted from the segmented point cloud. We also 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 transformation 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 http://tongtianta.site/paper/8117 4/24

2019/9/23 通天塔 LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain II。系统硬件 The framework proposed in this paper is validated using datasets gathered from Velodyne VLP-16 and HDL-64E 3D lidars. The VLP-16 measurement range is up to 100m with an accuracy of ± 3cm. It has a vertical ﬁeld of view (FOV) of 30◦( ) and a horizontal FOV of 360◦. The 16-channel sensor provides a vertical angular resolution of 2◦. The horizontal angular resolution varies from 0.1◦ to 0.4◦ based on the rotation rate. Throughout the paper, we choose a scan rate of 10Hz, which provides a horizontal angular resolution of 0.2◦. The HDL-64E (explored in this work via the KITTI dataset) also has a horizontal FOV of 360◦ but 48 more channels. The vertical FOV of the HDL-64E is 26.9◦. 本文提出的框架使用从Velodyne VLP-16和HDL-64E 3D激光雷达收集的数据集进行验证。VLP-16测量范围高达100米，精度为±3厘米。它的垂直视场（FOV）为30°（），水平视场为360°。16通道传感器提供2°的垂直角分辨率。根据旋转速率，水平角分辨率从0.1°到0.4°不等。在整篇论文中，我们选择10Hz的扫描速率，其提供0.2°的水平角分辨率。 HDL-64E（通过KITTI数据集在这项工作中探讨）也具有360°的水平FOV，但还有48个通道。 HDL-64E的垂直FOV为 26.9°。 The UGV used in this paper is the Clearpath Jackal. Powered by a 270 Watt hour Lithium battery, it has a maximum speed of 2.0m/s and maximum payload of 20kg. The Jackal is also equipped with a low-cost inertial measurement unit (IMU), the CH Robotics UM6 Orientation Sensor. 本文中使用的UGV是Clearpath Jackal。由270瓦时的锂电池供电，最大速度为2.0米/秒，最大有效载荷为20千克。Jackal还配备了低成本惯性测量单元（IMU），CH Robotics UM6方向传感器。 The proposed framework is validated on two computers: an Nvidia Jetson TX2 and a laptop with a 2.5GHz i74710MQ CPU. The Jetson TX2 is an embedded computing device that is equipped with an ARM Cortex-A57 CPU. The laptop CPU was selected to match the computing hardware used in [19] and [20]. The experiments shown in this paper use the CPUs of these systems only. 建议的框架在两台计算机上得到验证：Nvidia Jetson TX2和配备2.5GHz i74710MQ CPU的笔记本电脑。Jetson TX2是一款嵌入式计算设备，配备ARM Cortex-A57 CPU。选择笔记本电脑CPU以匹配[19]和[20]中使用的计算硬件。本文中显示的实验仅使用这些系统的CPU。 III. LIGHTWEIGHT LIDAR ODOMETRY AND MAPPING III。轻量级激光雷达测量和绘图 A. System Overview A.系统概述 An overview of the proposed framework is shown in Figure 1. The system receives input from a 3D lidar and outputs 6 DOF pose estimation. The overall system is divided into ﬁve modules. The ﬁrst, segmentation, takes a single scan’s point cloud and projects it onto a range image for segmentation. The segmented point cloud is then sent to the feature extraction module. Then, lidar odometry uses features extracted from the previous module to ﬁnd the transformation relating consecutive scans. The features are further processed in lidar mapping, which registers them to a global point cloud map. At last, the transform integration module fuses the pose estimation results from lidar odometry and lidar mapping and outputs the ﬁnal pose estimate. The proposed system seeks improved efﬁciency and accuracy for ground vehicles, with respect to the original, generalized LOAM framework of [19] and [20]. The details of these modules are introduced below. 拟议框架的概述如图1所示。系统接收来自3D激光雷达的输入并输出6 DOF姿势估计。整个系统分为五个模块。第一个分割采用单个扫描的点云，并将其投影到范围图像上进行分割。然后将分段的点云发送到特征提取模块。然后，激光雷达测距仪使用从前一模块中提取的特征来找到与连续扫描相关的变换。这些特征在激光雷达映射中进一步处理，激光雷达映射将它们注册到全局点云图。最后，变换积分模块融合了激光雷达测距和激光雷达测绘的姿态估计结果，并输出最 http://tongtianta.site/paper/8117 5/24

通天塔 LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain 2019/9/23 终的姿态估计。与[19]和[20]的原始广义LOAM框架相比，所提出的系统寻求提高地面车辆的效率和准确性。下面介绍这些模块的细节。 Fig. 2: Feature extraction process for a scan in noisy environment. The original point cloud is shown in (a). In (b), the red points are labeled as ground points. The rest of the points are the points that remain after segmentation. In (c), blue and yellow points indicate edge and planar features in . In (d), the green and pink points represent edge and planar features in and and respectively. 图2：在嘈杂环境中扫描的特征提取过程。原始点云显示在（a）中。在（b）中，红点被标记为地面点。其余的点是分割后剩余的点。在（c）中，蓝色和黄色点表示和中的边缘和平面特征。在（d）中，绿色和粉红色点分别代表和中的边缘和平面特征。 B. Segmentation B.细分 Let is ﬁrst projected onto a range image. The resolution of the projected range image is 1800 by 16, since the VLP-16 has horizontal and vertical angular resolution of 0.2◦ and 2◦ respectively. Each valid point is now represented by a unique pixel in the range image. The range value be the point cloud acquired at time t, where is a point in . in that is associated with represents the Euclidean distance from the corresponding point to the sensor. Since sloped terrain is common in many environments, we do not assume the ground is ﬂat. A column-wise evaluation of the range image, which can be viewed as ground plane estimation [22], is conducted for ground point extraction before segmentation. After this process, points that may represent the ground are labeled as ground points and not used for segmentation. 成为在时间t获取的点云，其中是中的一个点。首先投影到范围图像上。由于VLP-16 让的水平和垂直角分辨率分别为0.2°和2°，因此投影距离图像的分辨率为1800 x 16。中的每个有效点现在由范围图像中的唯一像素表示。与关联的范围值表示从对应点到传感器的欧几里德距离。由于倾斜的地形在许多环境中很常见，我们并不认为地面很普遍。在分割之前进行地面图像的逐列评估，其可以被视为地平面估计[22]，用于地面点提取。在此过程之后，可能代表地面的点被标记为地面点而不用于分段。 Then, an image-based segmentation method [23] is applied to the range image to group points into many clusters. Points from the same cluster are assigned a unique label. Note that the ground points are a special type of cluster. Applying segmentation to http://tongtianta.site/paper/8117 6/24

通天塔 LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain 2019/9/23 the point cloud can improve processing efﬁciency and feature extraction accuracy. Assuming a robot operates in a noisy environment, small objects, e.g., tree leaves, may form trivial and unreliable features, as the same leaf is unlikely to be seen in two consecutive scans. In order to perform fast and reliable feature extraction using the segmented point cloud, we omit the clusters that have fewer than 30 points. A visualization of a point cloud before and after segmentation is shown in Fig. 2. The original point cloud includes many points, which are obtained from surrounding vegetation that may yield unreliable features. After this process, only the points (Fig. 2(b)) that may represent large objects, e.g., tree trunks, and ground points are preserved for further processing. At the same time, only these points are saved in the range image. We also obtain Fig. 3: Two-step optimization for the lidar odometry module. from ground points. applying 然后，将基于图像的分割方法[23]应用于距离图像以将点分组为多个聚类。来自同一群集的点将分配唯一标签。请注意，地面点是一种特殊类型的群集。将分段应用于点云可以提高处理效率和特征提取精度。假设机器人在嘈杂的环境中操作，小的物体（例如树叶）可能形成琐碎且不可靠的特征，因为在两次连续扫描中不太可能看到相同的叶子。为了使用分段点云执行快速可靠的特征提取，我们省略了少于30个点的聚类。在分割之前和之后的点云的可视化在图2中示出。原始点云包括许多点，这些点是从周围植被获得的，可能产生不可靠的特征。在该过程之后，仅保留可表示大对象（例如树干）和地面点的点（图2（b））以进行进一步处理。同时，只有这些点保存在范围图像中。我们还获得了图 3：激光雷达测距模块的两步优化。首先通过匹配从地面点提取的平面特征来获得。然后使用从分段点提取的边缘特征估计 are then estimated using the edge features extracted from segmented points while is ﬁrst obtained by matching the planar features extracted as constraints. ，同时应用作为约束。 three properties for each point: (1) its label as a ground point or segmented point, (2) its column and row index in the range image, and (3) its range value. These properties will be utilized in the following modules. 每个点的三个属性：（1）其标签作为地面点或分段点，（2）其范围图像中的列和行索引，以及（3）其范围值。这些属性将在以下模块中使用。 C. Feature Extraction C.特征提取 The feature extraction process is similar to the method used in [20]. However, instead of extracting features from raw point clouds, we extract features from ground points and segmented points. Let S be the set of continuous points of row of the range image. Half of the points in S are on either side of computed during segmentation, we can evaluate the roughness of point . In this paper, we set to 10. Using the range values in S, from the same http://tongtianta.site/paper/8117 7/24

通天塔 LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain 2019/9/23 特征提取过程类似于[20]中使用的方法。但是，我们不是从原始点云中提取特征，而是从地面点和分段点提取特征。设S 是范围图像的同一行中的连续点集。S中的一半点位于的两侧。在本文中，我们将设置为10.使用在分割期间计算的范围值，我们可以评估S中点的粗糙度， To evenly extract features from all directions, we divide the range image horizontally into several equal sub-images. Then we sort the points in each row of the sub-image based on their roughness values c. Similar to LOAM, we use a threshold distinguish different types of features. We call the points with c larger than edge features, and the points with c smaller than to edge feature points with the maximum c, which do not belong to the ground, are selected from planar feature points with the minimum c, which may be labeled as either ground or segmented planar features. Then each row in the sub-image. points, are selected in the same way. Let and features are visualized in Fig. 2(d). We then extract from each row in the sub-image. Similarly, we extract from each row in the sub-image. Let and be the set of all edge and planar features from all sub-images. These edge features with the maximum c, which do not belong to the ground, planar features with the minimum c, which must be ground points, be the set of all edge and planar features from this process. Here, we have and . Features in and are shown in Fig. 2(c). In this paper, we divide the 360◦ range image into 6 are chosen to be 2, 4, 40 and 80 sub-images. Each sub-image has a resolution of 300 by 16. nFe , nFp , nFe and respectively. 为了从所有方向均匀地提取特征，我们将范围图像水平划分为几个相等的子图像。然后我们基于它们的粗糙度值c对子图像的每一行中的点进行排序。与LOAM类似，我们使用阈值来区分不同类型的功能。我们称c的点大于边缘特征，并且c的点小于平面特征。然后从子图像中的每一行中选择具有最大c的边缘特征点，其不属于地面。具有最小c的平面特征点（可以标记为地面或分段点）以相同的方式选择。让和成为所有子图像的所有边缘和平面特征的集合。这些特征在图2（d）中可视化。然后，我们从子图像中的每一行提取边缘特征，其中最大c不属于地面。类似地，我们从子图像中的每一行提取具有最小c的平面特征，该最小c必须是地面点。让和成为此过程。和的功能如图2（c）所示。在本文中，我中所有边和平面特征的集合。在这里，我们有们将360°范围图像分为6个子图像。每个子图像具有300乘16的分辨率.nFe，nFp，nFe和分别被选择为2,4,40和80。和 D. Lidar Odometry D. Lidar Odometry and The lidar odometry module estimates the sensor motion between two consecutive scans. The transformation between two scans is found by performing point-to-edge and pointto-plane scan-matching. In other words, we need to ﬁnd the corresponding features for points in of the previous scan. For the sake of brevity, the detailed procedures of ﬁnding these correspondences can be found in [20]. 激光雷达测距仪模块估计两次连续扫描之间的传感器运动。通过执行点对边和点对面扫描匹配找到两次扫描之间的转换。换句话说，我们需要从上一次扫描的特征集和中找到和中的点的相应特征。为简洁起见，可以在[20] 中找到发现这些对应关系的详细程序。 from feature sets and However, we note that a few changes can be made to improve feature matching accuracy and efﬁciency: 1) Label Matching: Since each feature in label from and planar patch as the correspondence. For an edge feature in clusters. Finding the correspondences in this way can help improve the matching accuracy. In other words, the matching is encoded with its label after segmentation, we only ﬁnd correspondences that have the same are used for ﬁnding a from segmented and . For planar features in , only points that are labeled as ground points in , its corresponding edge line is found in the http://tongtianta.site/paper/8117 8/24

分享到：

赞收藏

资料库

LeGO-LOAM_ Lightweight and Ground-Optimized Lidar Odometry and M....pdf

相关推荐

人工智能

热门标签

最新资料