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Unsupervised Monocular Visual-inertial Odometry Network
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Recently, unsupervised methods for monocular visual odometry (VO), with no need for quantities of expensive labeled ground truth, have attracted much attention. However, these methods are inadequate for long-term odometry task, due to the inherent limitation of only using monocular visual data and the inability to handle the error accumulation problem. By utilizing supplemental low-cost inertial measurements, and exploiting the multi-view geometric constraint and sequential constraint, an
doi:10.24963/ijcai.2020/321
dblp:conf/ijcai/HoernleGGLRR20
fatcat:patxy3qm3fgmnljhesby7o3574