Object-Level SLAM

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..

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.

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.

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.

Probabilistic Multi-View Fusion  of Active Stereo Depth Maps

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Jun Yang, Dong Li and Steven L. Waslander. 

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

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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.

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.