# Robust Flower Cluster Matching Using The Unscented Transform

arXiv: https://arxiv.org/abs/2503.20631 · HTML: https://mlsanity.com/paper/2503.20631/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Design of an Autonomous Precision Pollination Robot](https://mlsanity.com/paper/1808.10010/) (2018)
- [A Survey of Robotic Harvesting Systems and Enabling Technologies](https://mlsanity.com/paper/2207.10457/) (2022)
- [Learning to Simulate Tree-Branch Dynamics for Manipulation](https://mlsanity.com/paper/2306.03410/) (2023)
- [Design of Stickbug: a Six-Armed Precision Pollination Robot](https://mlsanity.com/paper/2404.03489/) (2024)

## Abstract (arXiv)

Monitoring flowers over time is essential for precision robotic pollination in agriculture. To accomplish this, a continuous spatial-temporal observation of plant growth can be done using stationary RGB-D cameras. However, image registration becomes a serious challenge due to changes in the visual appearance of the plant caused by the pollination process and occlusions from growth and camera angles. Plants flower in a manner that produces distinct clusters on branches. This paper presents a method for matching flower clusters using descriptors generated from RGB-D data and considers allowing for spatial uncertainty within the cluster. The proposed approach leverages the Unscented Transform to efficiently estimate plant descriptor uncertainty tolerances, enabling a robust image-registration process despite temporal changes. The Unscented Transform is used to handle the nonlinear transformations by propagating the uncertainty of flower positions to determine the variations in the descriptor domain. A Monte Carlo simulation is used to validate the Unscented Transform results, confirming our method's effectiveness for flower cluster matching. Therefore, it can facilitate improved robotics pollination in dynamic environments.