# BotanicGarden: A High-Quality Dataset for Robot Navigation in Unstructured Natural Environments

arXiv: https://arxiv.org/abs/2306.14137 · HTML: https://mlsanity.com/paper/2306.14137/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2023.

## Built on by (most cited first, 21 of 21)

- [Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset](https://mlsanity.com/paper/2309.13549/) (2023)
- [The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods](https://mlsanity.com/paper/2411.10546/) (2024)
- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [Seeing in the Dark: Benchmarking Egocentric 3D Vision with the Oxford Day-and-Night Dataset](https://mlsanity.com/paper/2506.04224/) (2025)
- [Advancing Off-Road Autonomous Driving: The Large-Scale ORAD-3D Dataset and Comprehensive Benchmarks](https://mlsanity.com/paper/2510.16500/) (2025)
- [Autonomous Driving in Unstructured Environments: How Far Have We Come?](https://mlsanity.com/paper/2410.07701/) (2024)
- [WildCross: A Cross-Modal Large Scale Benchmark for Place Recognition and Metric Depth Estimation in Natural Environments](https://mlsanity.com/paper/2603.01475/) (2026)
- [ROVER: A Multi-Season Dataset for Visual SLAM](https://mlsanity.com/paper/2412.02506/) (2024)
- [Survey on Datasets for Perception in Unstructured Outdoor Environments](https://mlsanity.com/paper/2404.18750/) (2024)
- [The Rosario Dataset v2: Multimodal Dataset for Agricultural Robotics](https://mlsanity.com/paper/2508.21635/) (2025)
- [TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions](https://mlsanity.com/paper/2506.21630/) (2025)
- [FT-NCFM: An Influence-Aware Data Distillation Framework for Efficient VLA Models](https://mlsanity.com/paper/2511.16233/) (2025)
- [The S3LI Vulcano Dataset: A Dataset for Multi-Modal SLAM in Unstructured Planetary Environments](https://mlsanity.com/paper/2601.19557/) (2026)
- [TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching](https://mlsanity.com/paper/2602.01501/) (2026)
- [PIG-Nav: Key Insights for Pretrained Image Goal Navigation Models](https://mlsanity.com/paper/2507.17220/) (2025)
- [WildfireX-SLAM: A Large-scale Low-altitude RGB-D Dataset for Wildfire SLAM and Beyond](https://mlsanity.com/paper/2510.27133/) (2025)
- [CropCraft: A Procedural World Generator for Robotic Simulation of Agricultural Tasks](https://mlsanity.com/paper/2511.02417/) (2025)
- [Nano-U: Efficient Terrain Segmentation for Tiny Robot Navigation](https://mlsanity.com/paper/2605.10210/) (2026)
- [DL-VINS-Factory: A Modular Framework for Learned Visual Front-Ends in Visual-Inertial SLAM](https://mlsanity.com/paper/2607.01757/) (2026)
- [LightLoc++: Sensor-Robust Representation Learning for Efficient Outdoor LiDAR Localization](https://mlsanity.com/paper/2608.15317/) (2026)
- [M3GA-Wild: A Large-Scale Dataset and Benchmark for Multi-Modal Multi-session Ground-to-Aerial Place Recognition in Forests](https://mlsanity.com/paper/2609.23003/) (2026)

## Abstract (arXiv)

The rapid developments of mobile robotics and autonomous navigation over the years are largely empowered by public datasets for testing and upgrading, such as sensor odometry and SLAM tasks. Impressive demos and benchmark scores have arisen, which may suggest the maturity of existing navigation techniques. However, these results are primarily based on moderate structured scenario testing. When transitioning to challenging unstructured environments, especially in GNSS-denied, texture-monotonous, and dense-vegetated natural fields, their performance can hardly sustain at a high level and requires further validation and improvement. To bridge this gap, we build a novel robot navigation dataset in a luxuriant botanic garden of more than 48000m2. Comprehensive sensors are used, including Gray and RGB stereo cameras, spinning and MEMS 3D LiDARs, and low-cost and industrial-grade IMUs, all of which are well calibrated and hardware-synchronized. An all-terrain wheeled robot is employed for data collection, traversing through thick woods, riversides, narrow trails, bridges, and grasslands, which are scarce in previous resources. This yields 33 short and long sequences, forming 17.1km trajectories in total. Excitedly, both highly-accurate ego-motions and 3D map ground truth are provided, along with fine-annotated vision semantics. We firmly believe that our dataset can advance robot navigation and sensor fusion research to a higher level.