Sensors 2017, 17(9), 1932; https://doi.org/10.3390/s17091932
Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor
1
College of Information Engineering, Northwest A&F University, Xianyang 712100, China
2
Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Xianyang 712100, China
3
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)
Abstract
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-TextKeywords:
3D LiDAR; object tracking; Kalman filter; adaptive particle filter
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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.
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