Active Semantic Mapping of Horticultural Environments Using Gaussian Splatting

TL;DR

This paper introduces an active 3D semantic mapping system for horticultural environments that combines Octomap and Gaussian Splatting, improving accuracy and efficiency for agricultural scene reconstruction.

Contribution

The work presents a novel integration of Octomap with 3D Gaussian Splatting for target-aware, high-fidelity scene reconstruction in agricultural robotics.

Findings

Outperforms occupancy-based methods in runtime and accuracy

Achieves 6.6% higher fruit-level F1 score without noise

Reduces runtime by 50% for scalable real-time mapping

Abstract

Semantic reconstruction of agricultural scenes plays a vital role in tasks such as phenotyping and yield estimation. However, traditional approaches that rely on manual scanning or fixed camera setups remain a major bottleneck in this process. In this work, we propose an active 3D reconstruction framework for horticultural environments using a mobile manipulator. The proposed system integrates the classical Octomap representation with 3D Gaussian Splatting to enable accurate and efficient target-aware mapping. While a low-resolution Octomap provides probabilistic occupancy information for informative viewpoint selection and collision-free planning, 3D Gaussian Splatting leverages geometric, photometric, and semantic information to optimize a set of 3D Gaussians for high-fidelity scene reconstruction. We further introduce simple yet effective strategies to enhance robustness against segmentation noise and reduce memory consumption. Simulation experiments demonstrate that our method outperforms purely occupancy-based approaches in both runtime efficiency and reconstruction accuracy, enabling precise fruit counting and volume estimation. Compared to a 0.01m-resolution Octomap, our approach achieves an improvement of 6.6% in fruit-level F1 score under noise-free conditions, and up to 28.6% under segmentation noise. Additionally, it achieves a 50% reduction in runtime, highlighting its potential for scalable, real-time semantic reconstruction in agricultural robotics.