Semantic SLAM-based Autonomous Tributary Navigation System Using 3D LiDAR Point Cloud for UAV

被引：0

作者：

Pak, Jeonghyeon ^{[1
,2
]}

Son, Hyoung Il ^{[1
,2
]}

机构：

[1] Chonnam Natl Univ, Dept Convergence Biosyst Engn, 77 Yongbong Ro, Gwangju 61186, South Korea

[2] Chonnam Natl Univ, Interdisciplinary Program IT Bio Convergence Syst, Yongbong Ro 77, Gwangju 61186, South Korea

来源：

2022 22ND INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2022) | 2022年

关键词：

3D LiDAR point cloud; semantic segmentation; simultaneous localization and mapping; tributary mapping; unmanned aerial vehicle;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Remote sensing methodologies are useful for managing natural systems. Among them, we focused on a methodology for collecting data by attaching three-dimensional (3D) light detection and ranging (LiDAR) sensors to unmanned aerial vehicles (UAVs) that can perform detection over a wide area with high mobility. In this study, we propose a semantic simultaneous localization and mapping (SLAM)-based autonomous driving system for tributary mapping by using a UAV equipped with a 3D LiDAR. As a natural system, tributaries are not studied extensively for diagnosing polluted watersheds and managing water quality. Therefore, it is necessary to develop a robust autonomous driving system for mapping tributary environments.

引用

页码：1380 / 1382

页数：3

共 6 条

[1]

Hess W, 2016, IEEE INT CONF ROBOT, P1271, DOI 10.1109/ICRA.2016.7487258

[2] Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs) [J].