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GLO-SLAM: a slam system optimally combining GPS and LiDAR odometry
Industrial Robot ( IF 1.8 ) Pub Date : 2021-06-11 , DOI: 10.1108/ir-12-2020-0272
Ruihao Lin , Junzhe Xu , Jianhua Zhang

Purpose

Large-scale and precise three-dimensional (3D) map play an important role in autonomous driving and robot positioning. However, it is difficult to get accurate poses for mapping. On one hand, the global positioning system (GPS) data are not always reliable owing to multipath effect and poor satellite visibility in many urban environments. In another hand, the LiDAR-based odometry has accumulative errors. This paper aims to propose a novel simultaneous localization and mapping (SLAM) system to obtain large-scale and precise 3D map.

Design/methodology/approach

The proposed SLAM system optimally integrates the GPS data and a LiDAR odometry. In this system, two core algorithms are developed. To effectively verify reliability of the GPS data, VGL (the abbreviation of Verify GPS data with LiDAR data) algorithm is proposed and the points from LiDAR are used by the algorithm. To obtain accurate poses in GPS-denied areas, this paper proposes EG-LOAM algorithm, a LiDAR odometry with local optimization strategy to eliminate the accumulative errors by means of reliable GPS data.

Findings

On the KITTI data set and the customized outdoor data set, the system is able to generate high-precision 3D map in both GPS-denied areas and areas covered by GPS. Meanwhile, the VGL algorithm is proved to be able to verify reliability of the GPS data with confidence and the EG-LOAM outperform the state-of-the-art baselines.

Originality/value

A novel SLAM system is proposed to obtain large-scale and precise 3D map. To improve the robustness of the system, the VGL algorithm and the EG-LOAM are designed. The whole system as well as the two algorithms have a satisfactory performance in experiments.



中文翻译:

GLO-SLAM:一个完美结合 GPS 和 LiDAR 里程计的 SLAM 系统

目的

大规模精确的三维(3D)地图在自动驾驶和机器人定位中发挥着重要作用。然而,很难获得准确的映射姿势。一方面,由于许多城市环境中的多径效应和较差的卫星能见度,全球定位系统 (GPS) 数据并不总是可靠的。另一方面,基于 LiDAR 的里程计具有累积误差。本文旨在提出一种新颖的同步定位和建图 (SLAM) 系统,以获得大规模和精确的 3D 地图。

设计/方法/方法

所提出的 SLAM 系统最佳地集成了 GPS 数据和 LiDAR 里程计。在该系统中,开发了两个核心算法。为了有效地验证该GPS数据的可靠性,VGL(的缩写V erify ģ PS数据与大号IDAR数据)算法和从激光雷达的点由算法使用。为了在 GPS 拒绝区域获得准确的位姿,本文提出 EG-LOAM 算法,一种具有局部优化策略的 LiDAR 里程计,通过可靠的 GPS 数据消除累积误差。

发现

在 KITTI 数据集和定制的室外数据集上,系统能够生成 GPS 拒绝区域和 GPS 覆盖区域的高精度 3D 地图。同时,VGL 算法被证明能够自信地验证 GPS 数据的可靠性,并且 EG-LOAM 优于最先进的基线。

原创性/价值

提出了一种新颖的 SLAM 系统来获得大比例尺和精确的 3D 地图。为了提高系统的鲁棒性,设计了VGL算法和EG-LOAM。整个系统以及两种算法在实验中都有令人满意的表现。

更新日期:2021-06-11
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