A Novel Technique For Indoor Object Distance Measurement By Using 3D Point Cloud and LiDAR

被引：0

作者：

Kim, Jisoo ^{[1
]}

Lee, Dongik ^{[1
]}

机构：

[1] Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea

来源：

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

基金：

新加坡国家研究基金会;

关键词：

region-based segmentation; 3D point cloud; indoor mobile robot; LiDAR; ROS; REGION-BASED SEGMENTATION;

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：

The SLAM (Simultaneous Localization and Mapping) technology has been widely exploited to collect information of location and environment for indoor mobile robots. Usually, SLAM has a single LiDAR(Light Detection and Ranging) sensor which reveals its vulnerability to complex terrain or distinction between objects. A possible solution to overcome this problem is the data fusion technique with LiDAR and depth cameras. This paper presents a novel data fusion technique with LiDAR data and 3D-point cloud data for estimating the surrounding object locations. In the proposed technique, the surrounding object location data are extracted using the region-based segmentation technique in real time using 3D-point cloud images. The effectiveness of the proposed algorithm is demonstrated with a set of experiments based on ROS (Robot Operating System).

引用

页码：1044 / 1048

页数：5

共 20 条

[1] Contour Detection and Hierarchical Image Segmentation [J].