实时激光雷达定位与建图：LOAM方法详解

需积分: 44 82 浏览量更新于2024-09-09 收藏 3.48MB PDF 举报

Lidar Odometry and Mapping in Real Time (LOAM) 是一项由 Ji Zhang 和 Sanjiv Singh 在 Robotics: Science and Systems 2014 年的会议上提出的关键贡献。这项研究关注于实时解决机器人在6自由度（6-DOF）运动中，通过2轴激光雷达进行精确定位和建图的问题。传统的SLAM（Simultaneous Localization and Mapping）方法往往依赖于离线批处理，通过闭环检测来校正累积误差，但 LOAM 提供了一种创新的解决方案。 LOAM 的核心挑战在于处理时间延迟的激光测距数据，这些数据不同时到达，可能导致地图的不一致性或误注册。为了解决这个问题，LOAM 采用了两个主要算法。第一个算法运行在较高的频率上，但精度较低，主要用于快速估计激光雷达的运动速度，提供实时的粗略估计。这种低精度但高频率的策略有助于保持系统响应的实时性。第二个算法的工作频率则相对较低，但其精度大大提高，负责精细匹配和点云的注册。这两个算法的结合至关重要，一个负责维持实时运动估计，另一个则确保地图的准确性和长期一致性。这种方法的优势在于无需依赖高级别的精确测距或惯性测量单元，从而降低了对硬件的需求，并保持了较低的计算复杂度。通过这种分层处理方式，LOAM 实现了低漂移和低运算负担，使得机器人能够在没有外部辅助传感器的情况下，如GPS或IMU，构建和维护精确的3D地图。这对于自动驾驶、无人机导航和其他对实时定位要求高的应用来说，具有显著的实际价值。LOAM 方法为实时SLAM领域设定了一个新的基准，成为了后续研究者和工程师们的重要参考资源。

Robotics: Science and Systems 2014

Berkeley, CA, USA, July 12-16, 2014

LOAM: Lidar Odometry and Mapping in Real-time

Ji Zhang and Sanjiv Singh

Abstract— We propose a real-time method for odometry and

mapping using range measurements from a 2-axis lidar moving

in 6-DOF. The problem is hard because the range measurements

are received at different times, and errors in motion estimation

can cause mis-registration of the resulting point cloud. To date,

coherent 3D maps can be built by off-line batch methods, often

using loop closure to correct for drift over time. Our method

achieves both low-drift and low-computational complexity with-

out the need for high accuracy ranging or inertial measurements.

The key idea in obtaining this level of performance is the

division of the complex problem of simultaneous localization and

mapping, which seeks to optimize a large number of variables

simultaneously, by two algorithms. One algorithm performs

odometry at a high frequency but low ﬁdelity to estimate velocity

of the lidar. Another algorithm runs at a frequency of an order of

magnitude lower for ﬁne matching and registration of the point

cloud. Combination of the two algorithms allows the method to

map in real-time. The method has been evaluated by a large set

of experiments as well as on the KITTI odometry benchmark.

The results indicate that the method can achieve accuracy at the

level of state of the art ofﬂine batch methods.

I. INTRODUCTION

3D mapping remains a popular technology [1]–[3]. Mapping

with lidars is common as lidars can provide high frequency

range measurements where errors are relatively constant irre-

spective of the distances measured. In the case that the only

motion of the lidar is to rotate a laser beam, registration of the

point cloud is simple. However, if the lidar itself is moving

as in many applications of interest, accurate mapping requires

knowledge of the lidar pose during continuous laser ranging.

One common way to solve the problem is using independent

position estimation (e.g. by a GPS/INS) to register the laser

points into a ﬁxed coordinate system. Another set of methods

use odometry measurements such as from wheel encoders or

visual odometry systems [4], [5] to register the laser points.

Since odometry integrates small incremental motions over

time, it is bound to drift and much attention is devoted to

reduction of the drift (e.g. using loop closure).

Here we consider the case of creating maps with low-

drift odometry using a 2-axis lidar moving in 6-DOF. A key

advantage of using a lidar is its insensitivity to ambient lighting

and optical texture in the scene. Recent developments in lidars

have reduced their size and weight. The lidars can be held by

a person who traverses an environment [6], or even attached

to a micro aerial vehicle [7]. Since our method is intended to

push issues related to minimizing drift in odometry estimation,

it currently does not involve loop closure.

The method achieves both low-drift and low-computational

complexity without the need for high accuracy ranging or

J. Zhang and S. Singh are with the Robotics Institute at Carnegie Mellon

University. Emails: zhangji@cmu.edu and ssingh@cmu.edu.

The paper is based upon work supported by the National Science Founda-

tion under Grant No. IIS-1328930.



Lidar

Mapping

Odometry

Fig. 1. The method aims at motion estimation and mapping using a moving

2-axis lidar. Since the laser points are received at different times, distortion is

present in the point cloud due to motion of the lidar (shown in the left lidar

cloud). Our proposed method decomposes the problem by two algorithms

running in parallel. An odometry algorithm estimates velocity of the lidar and

corrects distortion in the point cloud, then, a mapping algorithm matches and

registers the point cloud to create a map. Combination of the two algorithms

ensures feasibility of the problem to be solved in real-time.

inertial measurements. The key idea in obtaining this level of

performance is the division of the typically complex problem

of simultaneous localization and mapping (SLAM) [8], which

seeks to optimize a large number of variables simultaneously,

by two algorithms. One algorithm performs odometry at a high

frequency but low ﬁdelity to estimate velocity of the lidar. An-

other algorithm runs at a frequency of an order of magnitude

lower for ﬁne matching and registration of the point cloud.

Although unnecessary, if an IMU is available, a motion prior

can be provided to help account for high frequency motion.

Speciﬁcally, both algorithms extract feature points located on

sharp edges and planar surfaces, and match the feature points

to edge line segments and planar surface patches, respectively.

In the odometry algorithm, correspondences of the feature

points are found by ensuring fast computation. In the mapping

algorithm, the correspondences are determined by examining

geometric distributions of local point clusters, through the

associated eigenvalues and eigenvectors.

By decomposing the original problem, an easier problem is

solved ﬁrst as online motion estimation. After which, mapping

is conducted as batch optimization (similar to iterative closest

point (ICP) methods [9]) to produce high-precision motion

estimates and maps. The parallel algorithm structure ensures

feasibility of the problem to be solved in real-time. Further,

since the motion estimation is conducted at a higher frequency,

the mapping is given plenty of time to enforce accuracy.

When running at a lower frequency, the mapping algorithm

is able to incorporate a large number of feature points and use

sufﬁciently many iterations for convergence.

II. RELATED WORK

Lidar has become a useful range sensor in robot navigation

[10]. For localization and mapping, most applications use 2D

lidars [11]. When the lidar scan rate is high compared to

its extrinsic motion, motion distortion within the scans can

下载后可阅读完整内容，剩余8页未读，立即下载

chengwei0019

粉丝: 319
资源: 28

实时激光雷达定位与建图：LOAM方法详解

Lidar_odometry：使用LIDAR（光检测和测距）传感器和磁力计HMC5883导航机器人。 在wemos d1 mini atmega 16-32上独立运行。 我2018年的研发主题。@ laukik-hase @ ved29

Kitti部分数据集

lidar_tool

常见单线激光SLAM框架

我推荐20个比较流行的激光雷达融合感知模型源码地址

slam领域使用的lio-sam

在github 上有哪些出名的有关激光slam的项目

开源的3Dslam算法

lego-loam控制

lio-mapping

最新资源

Lidar_odometry：使用LIDAR（光检测和测距）传感器和磁力计HMC5883导航机器人。在wemos d1 mini atmega 16-32上独立运行。我2018年的研发主题。@ laukik-hase @ ved29