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Efficient View-Based SLAM Using Visual Loop Closures
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DOI：10.1109/TRO.2008.2004888.png)
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Abstract
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This paper presents a simultaneous localization and mapping algorithm suitable for large-scale visual navigation. The estimation process is based on the viewpoint augmented navigation (VAN) framework using an extended information filter. Cholesky factorization modifications are used to maintain a factor of the VAN information matrix, enabling efficient recovery of state estimates and covariances. The algorithm is demonstrated using data acquired by an autonomous underwater vehicle performing a visual survey of sponge beds. Loop-closure observations produced by a stereo vision system are used to correct the estimated vehicle trajectory produced by dead reckoning sensors.
Keywords:
Autonomous underwater vehicle (AUV) navigation
Cholesky factorization
extended information filter (EIF)
simultaneous localization and mapping (SLAM)
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