# Accurate Cutting-point Estimation for Robotic Lychee Harvesting through Geometry-aware Learning

arXiv: https://arxiv.org/abs/2404.00364 · HTML: https://mlsanity.com/paper/2404.00364/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 30 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection](https://mlsanity.com/paper/2112.00322/) (2021)
- [Generative Sparse Detection Networks for 3D Single-shot Object Detection](https://mlsanity.com/paper/2006.12356/) (2020)
- [Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards](https://mlsanity.com/paper/2112.04363/) (2021)
- [A Novel Perception and Semantic Mapping Method for Robot Autonomy in Orchards](https://mlsanity.com/paper/2308.16748/) (2023)

## Abstract (arXiv)

Accurately identifying lychee-picking points in unstructured orchard environments and obtaining their coordinate locations is critical to the success of lychee-picking robots. However, traditional two-dimensional (2D) image-based object detection methods often struggle due to the complex geometric structures of branches, leaves and fruits, leading to incorrect determination of lychee picking points. In this study, we propose a Fcaf3d-lychee network model specifically designed for the accurate localisation of lychee picking points. Point cloud data of lychee picking points in natural environments are acquired using Microsoft's Azure Kinect DK time-of-flight (TOF) camera through multi-view stitching. We augment the Fully Convolutional Anchor-Free 3D Object Detection (Fcaf3d) model with a squeeze-and-excitation(SE) module, which exploits human visual attention mechanisms for improved feature extraction of lychee picking points. The trained network model is evaluated on a test set of lychee-picking locations and achieves an impressive F1 score of 88.57%, significantly outperforming existing models. Subsequent three-dimensional (3D) position detection of picking points in real lychee orchard environments yields high accuracy, even under varying degrees of occlusion. Localisation errors of lychee picking points are within 1.5 cm in all directions, demonstrating the robustness and generality of the model.