Next Article in Journal
A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System
Next Article in Special Issue
A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability
Previous Article in Journal
Basic Simulation Environment for Highly Customized Connected and Autonomous Vehicle Kinematic Scenarios
Previous Article in Special Issue
Moving Object Detection in Heterogeneous Conditions in Embedded Systems
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(9), 1932;

Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor

College of Information Engineering, Northwest A&F University, Xianyang 712100, China
Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Xianyang 712100, China
College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, China
Authors to whom correspondence should be addressed.
Received: 27 June 2017 / Revised: 20 August 2017 / Accepted: 21 August 2017 / Published: 23 August 2017
(This article belongs to the Special Issue Sensors for Transportation)
Full-Text   |   PDF [13096 KB, uploaded 23 August 2017]   |  


Object tracking is a crucial research subfield in computer vision and it has wide applications in navigation, robotics and military applications and so on. In this paper, the real-time visualization of 3D point clouds data based on the VLP-16 3D Light Detection and Ranging (LiDAR) sensor is achieved, and on the basis of preprocessing, fast ground segmentation, Euclidean clustering segmentation for outliers, View Feature Histogram (VFH) feature extraction, establishing object models and searching matching a moving spherical target, the Kalman filter and adaptive particle filter are used to estimate in real-time the position of a moving spherical target. The experimental results show that the Kalman filter has the advantages of high efficiency while adaptive particle filter has the advantages of high robustness and high precision when tested and validated on three kinds of scenes under the condition of target partial occlusion and interference, different moving speed and different trajectories. The research can be applied in the natural environment of fruit identification and tracking, robot navigation and control and other fields. View Full-Text
Keywords: 3D LiDAR; object tracking; Kalman filter; adaptive particle filter 3D LiDAR; object tracking; Kalman filter; adaptive particle filter

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Huang, L.; Chen, S.; Zhang, J.; Cheng, B.; Liu, M. Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor. Sensors 2017, 17, 1932.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top