  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.
