Next Article in Journal
A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals
Next Article in Special Issue
Design of Plant Protection UAV Variable Spray System Based on Neural Networks
Previous Article in Journal
HEALPix-IA: A Global Registration Algorithm for Initial Alignment
Previous Article in Special Issue
Intelligent Spectroscopy System Used for Physicochemical Variables Estimation in Sugar Cane Soils
Article Menu
Issue 2 (January-2) cover image

Export Article

Open AccessArticle
Sensors 2019, 19(2), 428; https://doi.org/10.3390/s19020428

Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field

1
Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China
2
College of Mechanical and Automotive Engineering, Chuzhou University, Chuzhou 239000, China
3
School of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou 510006, China
*
Authors to whom correspondence should be addressed.
Received: 27 December 2018 / Revised: 17 January 2019 / Accepted: 18 January 2019 / Published: 21 January 2019
(This article belongs to the Collection Sensors in Agriculture and Forestry)
  |  
PDF [6055 KB, uploaded 21 January 2019]
  |     |  

Abstract

Fruit detection in real outdoor conditions is necessary for automatic guava harvesting, and the branch-dependent pose of fruits is also crucial to guide a robot to approach and detach the target fruit without colliding with its mother branch. To conduct automatic, collision-free picking, this study investigates a fruit detection and pose estimation method by using a low-cost red–green–blue–depth (RGB-D) sensor. A state-of-the-art fully convolutional network is first deployed to segment the RGB image to output a fruit and branch binary map. Based on the fruit binary map and RGB-D depth image, Euclidean clustering is then applied to group the point cloud into a set of individual fruits. Next, a multiple three-dimensional (3D) line-segments detection method is developed to reconstruct the segmented branches. Finally, the 3D pose of the fruit is estimated using its center position and nearest branch information. A dataset was acquired in an outdoor orchard to evaluate the performance of the proposed method. Quantitative experiments showed that the precision and recall of guava fruit detection were 0.983 and 0.948, respectively; the 3D pose error was 23.43° ± 14.18°; and the execution time per fruit was 0.565 s. The results demonstrate that the developed method can be applied to a guava-harvesting robot. View Full-Text
Keywords: guava detection; pose estimation; fully convolutional network; branch reconstruction; RGB-D sensor guava detection; pose estimation; fully convolutional network; branch reconstruction; RGB-D sensor
Figures

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Lin, G.; Tang, Y.; Zou, X.; Xiong, J.; Li, J. Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field. Sensors 2019, 19, 428.

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

1

Comments

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