On-the-Go Tree Detection and Geometric Traits Estimation with Ground Mobile Robots in Fruit Tree Groves

By-tree information gathering is an essential task in precision agriculture achieved by ground mobile sensors, but it can be time- and labor-intensive. In this paper we present an algorithmic framework to perform real-time and on-the-go detection of trees and key geometric characteristics (namely, width and height) with wheeled mobile robots in the field. Our method is based on the fusion of 2D domain-specific data (normalized difference vegetation index [NDVI] acquired via a red-green-near-infrared [RGN] camera) and 3D LiDAR point clouds, via a customized tree landmark association and parameter estimation algorithm. The proposed system features a multi-modal and entropy-based landmark correspondences approach, integrated into an underlying Kalman filter system to recognize the surrounding trees and jointly estimate their spatial and vegetation-based characteristics. Realistic simulated tests are used to evaluate our proposed algorithm's behavior in a variety of settings. Physical experiments in agricultural fields help validate our method's efficacy in acquiring accurate by-tree information on-the-go and in real-time by employing only onboard computational and sensing resources.

Robot-assisted Soil Apparent Electrical Conductivity Measurements in Orchards

Soil apparent electrical conductivity (ECa) is a vital metric in Precision Agriculture and Smart Farming, as it is used for optimal water content management, geological mapping, and yield prediction. Several existing methods seeking to estimate soil electrical conductivity are available, including physical soil sampling, ground sensor installation and monitoring, and the use of sensors that can obtain proximal ECa estimates. However, such methods can be either very laborious and/or too costly for practical use over larger field canopies. Robot-assisted ECa measurements, in contrast, may offer a scalable and cost-effective solution. In this work, we present one such solution that involves a ground mobile robot equipped with a customized and adjustable platform to hold an Electromagnetic Induction (EMI) sensor to perform semi-autonomous and on-demand ECa measurements under various field conditions. The platform is designed to be easily re-configurable in terms of sensor placement; results from testing for traversability and robot-to-sensor interference across multiple case studies help establish appropriate tradeoffs for sensor placement. Further, a developed simulation software package enables rapid and accessible estimation of terrain traversability in relation to desired EMI sensor placement. Extensive experimental evaluation across different fields demonstrates that the obtained robot-assisted ECa measurements are of high linearity compared with the ground truth (data collected manually by a handheld EMI sensor) by scoring more than $90\%$ in Pearson correlation coefficient in both plot measurements and estimated ECa maps generated by kriging interpolation. The proposed robotic solution supports autonomous behavior development in the field since it utilizes the ROS navigation stack along with the RTK GNSS positioning data and features various ranging sensors.

Centroid Distance Keypoint Detector for Colored Point Clouds

Keypoint detection serves as the basis for many computer vision and robotics applications. Despite the fact that colored point clouds can be readily obtained, most existing keypoint detectors extract only geometry-salient keypoints, which can impede the overall performance of systems that intend to (or have the potential to) leverage color information. To promote advances in such systems, we propose an efficient multi-modal keypoint detector that can extract both geometry-salient and color-salient keypoints in colored point clouds. The proposed CEntroid Distance (CED) keypoint detector comprises an intuitive and effective saliency measure, the centroid distance, that can be used in both 3D space and color space, and a multi-modal non-maximum suppression algorithm that can select keypoints with high saliency in two or more modalities. The proposed saliency measure leverages directly the distribution of points in a local neighborhood and does not require normal estimation or eigenvalue decomposition. We evaluate the proposed method in terms of repeatability and computational efficiency (i.e. running time) against state-of-the-art keypoint detectors on both synthetic and real-world datasets. Results demonstrate that our proposed CED keypoint detector requires minimal computational time while attaining high repeatability. To showcase one of the potential applications of the proposed method, we further investigate the task of colored point cloud registration. Results suggest that our proposed CED detector outperforms state-of-the-art handcrafted and learning-based keypoint detectors in the evaluated scenes. The C++ implementation of the proposed method is made publicly available at https://github.com/UCR-Robotics/CED_Detector.