license: mit
task_categories:
- robotics
language:
- en
tags:
- AI
- IROS
- Robotics
- Embodied
IROS 2022 Accepted Paper Meta Info Dataset
This dataset is collect from the IROS 2022-2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (https://ieeexplore.ieee.org/xpl/conhome/9981026/proceeding) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/iros2022). For researchers who are interested in doing analysis of IROS 2022 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the IROS 2022 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Equations Latex code and Papers Search Engine
Meta Information of Json File of Paper
{
"title": "Learning-based Localizability Estimation for Robust LiDAR Localization",
"detail_url": "https://ieeexplore.ieee.org/document/9982257/",
"author_list": ["Julian Nubert", "Etienne Walther", "Shehryar Khattak", "Marco Hutter", "Julian Nubert", "Etienne Walther", "Shehryar Khattak", "Marco Hutter"],
"abstract": "LiDAR-based localization and mapping is one of the core components in many modern robotic systems due to the direct integration of range and geometry, allowing for precise motion estimation and generation of high quality maps in real-time. Yet, as a consequence of insufficient environmental constraints present in the scene, this dependence on geometry can result in localization failure, happening ..."
}
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