H. Dhami et al.: Using UAV Mounted 3D LiDAR to Estimate Plant Height


Bio Information

Harnaik Dhami is currently a computer science PhD student at the University of Maryland, College Park. He works with his advisor Dr. Pratap Tokekar in the Robotics Algorithms & Autonomous Systems (RAAS) Lab. His research interests are currently the use of computer vision and machine learning practices in robotic systems.


Presentation Abstract

We present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV). Knowing the height of plants is crucial to monitor their overall health and growth cycles, especially for high-throughput plant phenotyping. We present a methodology for extracting plant heights from 3D LiDAR point clouds, specifically focusing on plot based phenotyping environments. We also present a toolchain that can be used to create phenotyping farms for use in Gazebo simulations. The tool creates a randomized farm with realistic 3D plant and terrain models.

We conducted a series of simulations and hardware experiments in controlled and natural settings. Our algorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on our GitHub repository. The full paper can be found here. The work in the paper was accepted to IROS 2020.

Authors: Harnaik Dhami, Kevin Yu, Tianshu Xu, Qian Zhu, Kshitiz Dhakal, James Friel, Song Li, and Pratap Tokekar


Video

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