This paper considers online object-level mapping using partial point-cloud observations obtained online in an unknown environment. We develop an approach for fully Convolutional Object Retrieval and Symmetry-AIded Registration (CORSAIR). Our model extends the Fully Convolutional Geometric Features (FCGF) model to learn a global object-shape embedding in addition to local point-wise features from the point-cloud observations. The global feature is used to retrieve a similar object from a category database, and the local features are used for robust pose registration between the observed and the retrieved object. Our formulation also leverages symmetries, present in the object shapes, to obtain promising local-feature pairs from different symmetry classes for matching. We present results from synthetic and real-world datasets with different object categories to verify the robustness of our method.

Capability: T1C1A: Multi-Modal Representations of Knowledge

Points of Contact: Nikolay Atanasov (PI) and Qiaojun Feng

Video: https://youtu.be/uIr4Q08BKqo 

Paper: https://arxiv.org/pdf/2103.06911

Citation: T. Zhao, Q. Feng, S. Jadhav and N. Atanasov, “CORSAIR: Convolutional Object Retrieval and Symmetry-AIded Registration,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021