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

小隔间

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

Default

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

没有准备

单独

invulnerable

偏见

glacial

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

Endemic

消耗

配偶