SiGNgapore - 基于标志的视觉导航交互数据集

中文摘要 AI 辅助

自主机器人在面向人类环境中的未来取决于其在没有预建地图的情况下导航的能力；利用人类为人类设计的导航辅助设施（如导航标志）对于实现自主性至关重要。本手稿介绍了一个独特的数据集，该数据集使用手持设备在新加坡的多个公共场所收集，代表了人类日常频繁出入的环境，并侧重于无地图导航中基于标志的决策制定。该数据集包括围绕导航标志及其所处复杂环境的场景的RGB图像、稀疏深度、里程计和IMU测量数据。此外，我们提供了56个长时程导航任务和超过450个以标志为中心的场景。所有数据均以人类可读的格式提供，并附有用于转换为ROS 2的实用脚本。最后，我们提供了测试环境的场地地图和GPS对齐的场景图。我们讨论了该数据集的潜在应用场景。

英文摘要

The future of autonomous robots in human-oriented environments depends on their ability to leverage existing navigational aids embedded in these environments, such as navigational signs, to navigate unfamiliar spaces without prior maps. Yet robots rarely exploit these cues, relying instead on pre-built maps for navigation. To advance sign- based visual navigation, we introduce a unique dataset, SiGNgapore, for benchmarking sign-based decision-making for navigation, collected across diverse public spaces in Singapore commonly encountered in daily life, including hospitals, public transport hubs, and shopping malls. SiGNgapore comprises 456 scenarios, each capturing a navigational sign and its surrounding environment, which collectively underpin 56 long-horizon navigation missions designed to evaluate sequential decision-making. Each scenario includes RGB, depth, IMU, and odometry data collected using a handheld device. We additionally provide venue maps and GPS-aligned scene graphs of the test environments. Beyond benchmarking sign-guided sequential decision-making, SiGNgapore supports research into visual understanding of navigational signs and into navigation approaches that integrate signage cues with prior maps.

发表机构

• National University of Singapore（新加坡国立大学）

机构由 AI 辅助整理，请以论文原文为准。