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

TL;DR

BotanicGarden is a comprehensive dataset featuring diverse unstructured natural environments, designed to improve robot navigation and sensor fusion in challenging outdoor terrains.

Contribution

The paper introduces a novel, high-quality dataset with extensive sensor data and ground truth in natural environments, addressing limitations of existing datasets for unstructured outdoor navigation.

Findings

Provides 17.1 km of diverse natural environment data

Includes highly accurate ego-motion and 3D map ground truth

Offers fine-annotated vision semantics for advanced research

Abstract

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.