Semantic Mapping for Orchard Environments by Merging Two-Sides Reconstructions of Tree Rows

08/31/2018
by   Wenbo Dong, et al.
0

Measuring semantic traits for phenotyping is an essential but labor-intensive activity in horticulture. Researchers often rely on manual measurements which may not be accurate for tasks such as measuring tree volume. To improve the accuracy of such measurements and to automate the process, we consider the problem of building coherent three dimensional (3D) reconstructions of orchard rows. Even though 3D reconstructions of side views can be obtained using standard mapping techniques, merging the two side-views is difficult due to the lack of overlap between the two partial reconstructions. Our first main contribution in this paper is a novel method that utilizes global features and semantic information to obtain an initial solution aligning the two sides. Our mapping approach then refines the 3D model of the entire tree row by integrating semantic information common to both sides, and extracted using our novel robust detection and fitting algorithms. Next, we present a vision system to measure semantic traits from the optimized 3D model that is built from the RGB or RGB-D data captured by only a camera. Specifically, we show how canopy volume, trunk diameter, tree height and fruit count can be automatically obtained in real orchard environments. The experiment results from multiple datasets quantitatively demonstrate the high accuracy and robustness of our method.

READ FULL TEXT
research
04/16/2018

Tree Morphology for Phenotyping from Semantics-Based Mapping in Orchard Environments

Measuring tree morphology for phenotyping is an essential but labor-inte...
research
09/18/2017

Matterport3D: Learning from RGB-D Data in Indoor Environments

Access to large, diverse RGB-D datasets is critical for training RGB-D s...
research
02/13/2023

An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection

Tree fruit breeding is a long-term activity involving repeated measureme...
research
10/05/2022

SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations

Accurate mapping of large-scale environments is an essential building bl...
research
12/08/2020

Multiple Hypothesis Semantic Mapping for Robust Data Association

In this paper, we present a semantic mapping approach with multiple hypo...
research
03/26/2019

High-quality Instance-aware Semantic 3D Map Using RGB-D Camera

We present a mapping system capable of constructing detailed instance-le...
research
08/13/2018

Vision-Based Preharvest Yield Mapping for Apple Orchards

We present an end-to-end computer vision system for mapping yield in an ...

Please sign up or login with your details

Forgot password? Click here to reset