A convex relaxation for approximate global optimization in simultaneous localization and mapping
Modern approaches to simultaneous localization and mapping (SLAM) formulate the inference problem as a high-dimensional but sparse nonconvex M-estimation, and then apply general first- or second-order smooth optimization methods to recover a local minimizer of the objective function. The performance...
| Main Authors: | , , |
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| Other Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE),
2017-03-20T15:27:44Z.
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| Subjects: | |
| Online Access: | Get fulltext |