Localization and Mapping using
Instance-specific Mesh Models


Qiaojun Feng
Yue Meng
Mo Shan
Nikolay Atanasov
Department of Electrical and Computer Engineering
Contextual Robotics Institute
University of California, San Diego
In IROS 2019

This paper focuses on building semantic maps, containing object poses and shapes, using a monocular camera. This is an important problem because robots need rich understanding of geometry and context for higher level tasks. Our contribution is an instance-specific mesh model of object shape that can be optimized online based on semantic information extracted from camera images. Multi-view constraints on the object shape are obtained by detecting objects and extracting category-specific keypoints and segmentation masks. We show that the errors between projections of the mesh model and the observed keypoints and masks can be differentiated in order to obtain accurate instance-specific object shapes.
[Paper]

Video

Poster

Acknowledgements

We gratefully acknowledge support from ARL DCIST CRA W911NF17-2-0181 and ONR N00014-18-1-2828.
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