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Object-Level SLAM

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Multi-view 3D Object Reconstruction and Uncertainty Modelling
 
Ziwei Lia and Steven L. Waslander
2023

We propose a 3d object modeling approach that relies on neural implicit representation and provides both an object reconstruction and an uncertainty measure for each object..

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6D Pose Estimation for Textureless Objects on RGB Frames using Multi-View Optimization
 
Jun Yang, Wenjie Xue, Sahar Ghavidel, and Steven L. Waslander
International Conference on Robotics and Automation (ICRA), 2023..

We introduce a novel 6D object pose estimation framework that decouples the problem into a sequential two-step process. We use only RGB images acquired from multiple viewpoints.

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POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes
 
Jingxing Qian, Veronica Chatrath, Jun Yang, James Servos, Angela P Schoellig, Steven L Waslander
Robotics: Science and Systems (RSS). 2022.

We propose a framework that introduces a novel probabilistic object state representation to track object pose changes in semi-static scenes.

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Next-Best-View (NBV) Prediction for Highly Reflective Objects
 
Jun Yang and Steven L. Waslander
International Conference on Robotics and Automation (ICRA), 2022

In this work, we propose a next-best-view framework to strategically select camera viewpoints for completing depth data on reflective objects.

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Probabilistic Multi-View Fusion  of Active Stereo Depth Maps

Jun Yang, Dong Li and Steven L. Waslander. 

IEEE Robotics and Automation Letters (RA-L), 2021

In this work, we propose a probabilistic framework for scene reconstruction in robotic bin-picking. We estimate the depth data uncertainty and incorporated into a probabilistic model for  incrementally updating the scene.

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ROBI: Reflective Object In Bins Dataset

Jun Yang, Yizhou Gao, Dong Li and Steven L. Waslander.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.

In this paper, we present the ROBI dataset, a public dataset for 6D object pose estimation and multi-view depth fusion.. The dataset includes texture-less, highly reflective industrial parts in robotic bin-picking scenarios.

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