# GND: Global Navigation Dataset with Multi-Modal Perception and Multi-Category Traversability in Outdoor Campus Environments

arXiv: https://arxiv.org/abs/2409.14262 · HTML: https://mlsanity.com/paper/2409.14262/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2024.

## Built on by (most cited first, 10 of 10)

- [TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments](https://mlsanity.com/paper/2602.02459/) (2026)
- [MetricNet: Recovering Metric Scale in Generative Navigation Policies](https://mlsanity.com/paper/2509.13965/) (2025)
- [Wanderland: Geometrically Grounded Simulation for Open-World Embodied AI](https://mlsanity.com/paper/2511.20620/) (2025)
- [YOPO-Nav: Visual Navigation using 3DGS Graphs from One-Pass Videos](https://mlsanity.com/paper/2512.09903/) (2025)
- [A Semantic Observer Layer for Autonomous Vehicles: Pre-Deployment Feasibility Study of VLMs for Low-Latency Anomaly Detection](https://mlsanity.com/paper/2603.28888/) (2026)
- [Paired-CSLiDAR: Height-Stratified Registration for Cross-Source Aerial-Ground LiDAR Pose Refinement](https://mlsanity.com/paper/2605.00634/) (2026)
- [From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation](https://mlsanity.com/paper/2606.12603/) (2026)
- [NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation](https://mlsanity.com/paper/2607.19695/) (2026)
- [EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness](https://mlsanity.com/paper/2607.19880/) (2026)
- [RECAST: Recasting Vision-Language Semantics into an Actionable Cost Map for Robot Navigation](https://mlsanity.com/paper/2609.32595/) (2026)

## Abstract (arXiv)

Navigating large-scale outdoor environments requires complex reasoning in terms of geometric structures, environmental semantics, and terrain characteristics, which are typically captured by onboard sensors such as LiDAR and cameras. While current mobile robots can navigate such environments using pre-defined, high-precision maps based on hand-crafted rules catered for the specific environment, they lack commonsense reasoning capabilities that most humans possess when navigating unknown outdoor spaces. To address this gap, we introduce the Global Navigation Dataset (GND), a large-scale dataset that integrates multi-modal sensory data, including 3D LiDAR point clouds and RGB and 360-degree images, as well as multi-category traversability maps (pedestrian walkways, vehicle roadways, stairs, off-road terrain, and obstacles) from ten university campuses. These environments encompass a variety of parks, urban settings, elevation changes, and campus layouts of different scales. The dataset covers approximately 2.7km2 and includes at least 350 buildings in total. We also present a set of novel applications of GND to showcase its utility to enable global robot navigation, such as map-based global navigation, mapless navigation, and global place recognition.