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IDF-MFL: Infrastructure-free and Drift-free Magnetic Field Localization for Mobile Robot
Hongming Shen, Zhenyu Wu, Wei Wang +3
In recent years, infrastructure-based localization methods have achieved significant progress thanks to their reliable and drift-free localization capability. However, the pre-inst…
NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping
Jun Zhang, Huayang Zhuge, Yiyao Liu +12
Simultaneous Localization and Mapping (SLAM) is moving towards a robust perception age. However, LiDAR- and visual- SLAM may easily fail in adverse conditions (rain, snow, smoke an…
Category-level Shape Estimation for Densely Cluttered Objects
Zhenyu Wu, Ziwei Wang, Jiwen Lu +1
Accurately estimating the shape of objects in dense clutters makes important contribution to robotic packing, because the optimal object arrangement requires the robot planner to a…
Smart Explorer: Recognizing Objects in Dense Clutter via Interactive Exploration
Zhenyu Wu, Ziwei Wang, Zibu Wei +2
Recognizing objects in dense clutter accurately plays an important role to a wide variety of robotic manipulation tasks including grasping, packing, rearranging and many others. Ho…