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

TL;DR

The paper introduces GND, a comprehensive large-scale dataset with multi-modal sensory data and traversability maps from diverse outdoor campus environments, aiming to enhance robot navigation capabilities beyond pre-defined maps.

Contribution

The paper presents GND, a new large-scale dataset integrating multi-modal sensory data and traversability maps for outdoor environments, enabling advanced navigation applications.

Findings

GND covers 2.7 km² with 350+ buildings across diverse environments.

It supports applications like map-based and mapless navigation.

Demonstrates utility in global robot navigation tasks.

Abstract

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.