# Benchmark of visual and 3D lidar SLAM systems in simulation environment for vineyards

arXiv: https://arxiv.org/abs/2107.05283 · HTML: https://mlsanity.com/paper/2107.05283/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2021.

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

- [Keypoint Semantic Integration for Improved Feature Matching in Outdoor Agricultural Environments](https://mlsanity.com/paper/2503.08843/) (2025)
- [Visual-Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop Closing](https://mlsanity.com/paper/2408.01716/) (2024)
- [Collection and Evaluation of a Long-Term 4D Agri-Robotic Dataset](https://mlsanity.com/paper/2211.14013/) (2022)
- [Semantic-Aware Particle Filter for Reliable Vineyard Robot Localisation](https://mlsanity.com/paper/2509.18342/) (2025)
- [Semantic Landmark Particle Filter for Robot Localisation in Vineyards](https://mlsanity.com/paper/2603.10847/) (2026)

## Abstract (arXiv)

In this work, we present a comparative analysis of the trajectories estimated from various Simultaneous Localization and Mapping (SLAM) systems in a simulation environment for vineyards. Vineyard environment is challenging for SLAM methods, due to visual appearance changes over time, uneven terrain, and repeated visual patterns. For this reason, we created a simulation environment specifically for vineyards to help studying SLAM systems in such a challenging environment. We evaluated the following SLAM systems: LIO-SAM, StaticMapping, ORB-SLAM2, and RTAB-MAP in four different scenarios. The mobile robot used in this study equipped with 2D and 3D lidars, IMU, and RGB-D camera (Kinect v2). The results show good and encouraging performance of RTAB-MAP in such an environment.