Titlebook: Intelligent Autonomous Systems 18; Volume 2 Proceedings Soon-Geul Lee,Jinung An,Joo H. Kim Conference proceedings 2024 The Editor(s) (if ap

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LTS-NET: End-to-End Unsupervised Learning of?Long-Term 3D Stable Objects specifically distinguishing between static and dynamic objects. Understanding object stability is key for mobile robots since long-term stable objects can be exploited as landmarks for long-term localisation. Our pipeline includes a labelling method that utilizes historical data from the environmen

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EA-Repose: Efficient and?Accurate Feature-Metric-Based 6D Object Pose Refinement via?Deep Reinforcemthm as the solver. However, they may easily ignore the importance of the damping parameter . factor, which affects the accuracy and efficiency of prediction. In this paper, we present a coarse-to-fine feature-metric-based 6D object pose refinement framework, which utilizes the intermediate layers to

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Data Aggregation (DAgger) Algorithm Using Adversarial Agent Policy for?Dynamic Situationsating diverse situations and does not account for improvements in the ego agent’s policy during training. To address these limitations, a method has been proposed that involves training by modeling the dynamic obstacle’s behavior as the adversarial agent policy. However, this method has been applied

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