# Keypoint Semantic Integration for Improved Feature Matching in Outdoor Agricultural Environments

arXiv: https://arxiv.org/abs/2503.08843 · HTML: https://mlsanity.com/paper/2503.08843/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [R2D2: Repeatable and Reliable Detector and Descriptor](https://mlsanity.com/paper/1906.06195/) (2019)
- [OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association](https://mlsanity.com/paper/2103.02440/) (2021)
- [SFD2: Semantic-guided Feature Detection and Description](https://mlsanity.com/paper/2304.14845/) (2023)
- [Tree Detection and Diameter Estimation Based on Deep Learning](https://mlsanity.com/paper/2210.17424/) (2022)
- [Benchmark of visual and 3D lidar SLAM systems in simulation environment for vineyards](https://mlsanity.com/paper/2107.05283/) (2021)
- [ROVER: A Multi-Season Dataset for Visual SLAM](https://mlsanity.com/paper/2412.02506/) (2024)
- [Semantic SuperPoint: A Deep Semantic Descriptor](https://mlsanity.com/paper/2211.01098/) (2022)

## Built on by (most cited first, 5 of 5)

- [Semantic-Aware Particle Filter for Reliable Vineyard Robot Localisation](https://mlsanity.com/paper/2509.18342/) (2025)
- [Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey](https://mlsanity.com/paper/2601.22198/) (2026)
- [Semantic Landmark Particle Filter for Robot Localisation in Vineyards](https://mlsanity.com/paper/2603.10847/) (2026)
- [Calibration-Informative Region Selection for Online LiDAR--Camera Calibration in Agricultural Environments](https://mlsanity.com/paper/2605.23580/) (2026)
- [Misanthrope: A Privacy-Preserving Keypoint Detector](https://mlsanity.com/paper/2608.23012/) (2026)

## Abstract (arXiv)

Robust robot navigation in outdoor environments requires accurate perception systems capable of handling visual challenges such as repetitive structures and changing appearances. Visual feature matching is crucial to vision-based pipelines but remains particularly challenging in natural outdoor settings due to perceptual aliasing. We address this issue in vineyards, where repetitive vine trunks and other natural elements generate ambiguous descriptors that hinder reliable feature matching. We hypothesise that semantic information tied to keypoint positions can alleviate perceptual aliasing by enhancing keypoint descriptor distinctiveness. To this end, we introduce a keypoint semantic integration technique that improves the descriptors in semantically meaningful regions within the image, enabling more accurate differentiation even among visually similar local features. We validate this approach in two vineyard perception tasks: (i) relative pose estimation and (ii) visual localisation. Across all tested keypoint types and descriptors, our method improves matching accuracy by 12.6%, demonstrating its effectiveness over multiple months in challenging vineyard conditions.