Abstract
People detection is an essential technique for person-following mobile robots in applications of human-friendly services and collaborative tasks. 2D light detection and ranging (LiDAR) sensors are useful for these applications, especially for applications that detect and follow people while maintaining a suitable distance, at a close range, using accurate range measurements. In this study, we propose a method of human-leg detection in 3D feature space for a person-following mobile robot equipped with a 2D LiDAR sensor. We also propose an improved LiDAR scan segmentation technique to extract segments of human leg candidates. The newly proposed method generates a feature vector with the attributes of leg shapes and learns a classification boundary in 3D feature space. Experimental results indicate that the proposed method successfully describes the target dataset and provides accurate leg detection. This study demonstrates that human legs can be detected with improved accuracy by learning the classification boundary in 3D feature space.
| Original language | English |
|---|---|
| Pages (from-to) | 1299-1307 |
| Number of pages | 9 |
| Journal | International Journal of Precision Engineering and Manufacturing |
| Volume | 21 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2020 Jul 1 |
Bibliographical note
Funding Information:This work was supported in part by the NRF, MSIP (NRF-2017R1A2A1A17069329), and was also supported by the Agriculture, Food and Rural Affairs Research Center Support Program (Project No. 714002-07), Ministry of Agriculture, Food and Rural Affairs.
Publisher Copyright:
© 2020, Korean Society for Precision Engineering.
Keywords
- Human-leg detection
- LiDAR
- Mobile service robot
- One-class classification
- Person following
- Support vector data description
ASJC Scopus subject areas
- Mechanical Engineering
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering