Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection

Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection

© 2016 IEEE. Semidefinite Programming (SDP) and Sums-of-Squ-ares (SOS) relaxations have led to certifiably optimal non-minimal solvers for several robotics and computer vision problems. However, most non-minimal solvers rely on least squares formulations, and, as a result, are brittle against outlie...

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Bibliographic Details
Main Authors: Yang, Heng, Antonante, Pasquale, Tzoumas, Vasileios, Carlone, Luca
Other Authors: Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
Online Access:https://hdl.handle.net/1721.1/136232

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https://hdl.handle.net/1721.1/136232

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