Localization and mapping in urban area based on 3D point cloud of autonomous vehicles

被引：0

作者：

Wang M.-L. ^{[1
]}

Li Y. ^{[1
]}

Yang Y. ^{[1
]}

Zhu H. ^{[1
]}

Liu T. ^{[1
]}

机构：

[1] Key Laboratory of Intelligent Control and Decision of Complex System, School of Automation, Beijing Institute of Technology, Beijing

来源：

Journal of Beijing Institute of Technology (English Edition) | 2016年 / 25卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Gaussian model; ICP algorithm; Rao-Blackwellized particle filter (RBPF); Simultaneous localization and mapping (SLAM); Urban area; VoxelGrid filter;

D O I：

10.15918/j.jbit1004-0579.201625.0405

中图分类号：

学科分类号：

摘要：

In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping (SLAM) algorithm, which uses a VoxelGrid filter to down sample the point cloud data, with the combination of iterative closest points (ICP) algorithm and Gaussian model for particles updating, the matching between the local map and the global map to quantify particles' importance weight. The crude estimation by using ICP algorithm can find the high probability area of autonomous vehicles' poses, which would decrease particle numbers, increase algorithm speed and restrain particles' impoverishment. The calculation of particles' importance weight based on matching of attribute between grid maps is simple and practicable. Experiments carried out with the autonomous vehicle platform validate the effectiveness of our approaches. © 2016 Beijing Institute of Technology.

引用

页码：473 / 482

页数：9

共 14 条

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