Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 4, Issue 3, Pages 2296-2303Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2901987
Keywords
Robotics in agriculture and forestry; deep learning in robotics and automation; visual tracking; mapping; object detection; segmentation and categorization
Categories
Funding
- Army Research Laboratory, Distributed and Collaborative Intelligent Systems and Technology Collaborative Research Alliance [W911NF-17-2-0181]
- Army Research Office [W911NF-13-1-0350]
- United States Department of Agriculture [2015-67021-23857]
- National Science Foundation [IIS-1138847]
- Qualcomm Research
- C-BRIC
- DARPA
- Australian Centre for Field Robotics, The University of Sydney
- NIFA [2015-67021-23857, 810574] Funding Source: Federal RePORTER
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In this letter, we present a cheap, lightweight, and fast fruit counting pipeline. Our pipeline relies only on a monocular camera, and achieves counting performance comparable to a state-of-the-art fruit counting system that utilizes an expensive sensor suite including a monocular camera, LiDAR and GPS/INS on amango dataset. Our pipeline begins with a fruit and tree trunk detection component that uses state-of-the-art convolutional neural networks (CNNs). It then tracks fruits and tree trunks across images, with a Kalman Filter fusing measurements from the CNN detectors and an optical flow estimator. Finally, fruit count and map are estimated by an efficient fruit-as-feature semantic structure from motion algorithm that converts two-dimensional (2-D) tracks of fruits and trunks into 3-D landmarks, and uses these landmarks to identify double counting scenarios. There are many benefits of developing such a low cost and lightweight fruit counting system, including applicability to agriculture in developing countries, where monetary constraints or unstructured environments necessitate cheaper hardware solutions.
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