A LIDAR-BASED 3D INDOOR MAPPING FRAMEWORK WITH MISMATCH DETECTION AND OPTIMIZATION

被引：0

作者：

Wang, Zhiyong ^{[1
]}

Liu, Weiquan ^{[1
]}

Wen, Chenglu ^{[1
]}

Shi, Yongfei ^{[2
]}

Yan, Xiaocheng ^{[3
]}

Tan, Jinbin ^{[1
]}

Wang, Cheng ^{[1
]}

Li, Jonathan ^{[4
]}

机构：

[1] Xiamen Univ, Sch Informat, Fujian Key Lab Sensing & Comp Smart Cities, Xiamen, Peoples R China

[2] Xiamen Rail Transit Grp Co Ltd, Xiamen, Peoples R China

[3] Xiamen Sizong Construct Co Ltd, Xiamen, Peoples R China

[4] Univ Waterloo, Dept Geog & Environm Management, Waterloo, ON, Canada

来源：

2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

SLAM; mismatch detection; deep learning; intensity-assisted registration optimization;

D O I：

10.1109/IGARSS46834.2022.9883319

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

In this paper, we propose a novel LiDAR-based mapping framework for geometry-featureless scenarios, combining learning-based mismatch detection and intensity-assisted registration optimization. The mismatch detection method hybridizes point cloud features and temporal features of pose to detect the mismatch position, and the optimization method uses the intensity of captured road markers by LiDAR to optimize mismatch pose. Experiments on underground garages data demonstrate that the proposed method performs better localization and mapping accuracy than the LOAM.

引用

页码：7499 / 7502

页数：4

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