# A Novel Perception and Semantic Mapping Method for Robot Autonomy in Orchards

arXiv: https://arxiv.org/abs/2308.16748 · HTML: https://mlsanity.com/paper/2308.16748/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2023.

## Built on by (most cited first, 2 of 2)

- [Accurate Cutting-point Estimation for Robotic Lychee Harvesting through Geometry-aware Learning](https://mlsanity.com/paper/2404.00364/) (2024)
- [Fiducial Marker Splatting for High-Fidelity Robotics Simulations](https://mlsanity.com/paper/2508.17012/) (2025)

## Abstract (arXiv)

Agricultural robots must navigate challenging dynamic and semi-structured environments. Recently, environmental modeling using LiDAR-based SLAM has shown promise in providing highly accurate geometry. However, how this chaotic environmental information can be used to achieve effective robot automation in the agricultural sector remains unexplored. In this study, we propose a novel semantic mapping and navigation framework for achieving robotic autonomy in orchards. It consists of two main components: a semantic processing module and a navigation module. First, we present a novel 3D detection network architecture, 3D-ODN, which can accurately process object instance information from point clouds. Second, we develop a framework to construct the visibility map by incorporating semantic information and terrain analysis. By combining these two critical components, our framework is evaluated in a number of key horticultural production scenarios, including a robotic system for in-situ phenotyping and daily monitoring, and a selective harvesting system in apple orchards. The experimental results show that our method can ensure high accuracy in understanding the environment and enable reliable robot autonomy in agricultural environments.