Learning Off-Road Terrain Traversability With Self-Supervisions Only

Author:

Seo Junwon1ORCID,Sim Sungdae1,Shim Inwook1ORCID

Affiliation:

1. Agency for Defense Development, Daejeon, Republic of Korea

Funder

Agency For Defense Development

Korean Government in 2023

Korea Institute for Advancement of Technologe

Korea Government

HRD Program for Industrial Innovation

National Research Foundation of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Reference33 articles.

1. A simple framework for contrastive learning of visual representations;chen;Proc Int Conf Mach Learn,0

2. Fastflow: Unsupervised anomaly detection and localization via 2D normalizing flows;yu,2021

3. Navigation planning for legged robots in challenging terrain

4. Are we ready for autonomous driving? The KITTI vision benchmark suite

5. Sinkhorn distances: Lightspeed computation of optimal transport;cuturi;Proc Adv Neural Inf Process Syst,0

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1. UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

2. W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics;IEEE Robotics and Automation Letters;2024-06

3. V-STRONG: Visual Self-Supervised Traversability Learning for Off-road Navigation;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

4. DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

5. Learning Self-Supervised Traversability With Navigation Experiences of Mobile Robots: A Risk-Aware Self-Training Approach;IEEE Robotics and Automation Letters;2024-05

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