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Efficient View-Based SLAM Using Visual Loop Closures

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4 Author(s)
Ian Mahon ; ARC Centre of Excellence for Autonomous Syst., Sydney Univ., Sydney, NSW ; Stefan B. Williams ; Oscar Pizarro ; Matthew Johnson-Roberson

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.

Published in:

IEEE Transactions on Robotics  (Volume:24 ,  Issue: 5 )