AI & ML
SiGNgapore - An Interactive Dataset for Sign-based Visual Navigation
arXiv cs.RO0 views
arXiv:2610.09488v1 Announce Type: new Abstract: The future of autonomous robots in human-oriented environments depends on their ability to navigate in the absence of prebuilt maps; exploiting navigational aids, such as navigational signs, designed by humans for humans is critical for enabling autonomy. This manuscript introduces a unique dataset collected using a handheld device, at various public spaces across Singapore, representing environments that humans frequent daily, and focusing on sign-centric decision making for mapless navigation. The dataset includes RGB, sparse depth, odometry and IMU measurements of scenarios centered around navigational signs and the complex environments where they are placed. Additionally, we provide 56 long-horizon navigation missions and over 450 sign-centric scenarios. All data is provided in a human- readable format, as well as utility scripts for conversion to ROS 2. Lastly, we provide venue maps and GPS aligned scene graphs of the test environments. We discuss the potential use cases for this dataset.
Source:https://arxiv.org/abs/2610.09488PreviousTMT: Runtime Backdoor Detection for Vision-Language-Action Policies on Unseen TasksNextSparse Feature Policy Unlearning Mitigates State Hallucination in Vision-Language-Action Models
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