incremental SAM
iSAM, or Incremental Smoothing and Mapping, is a novel incremental optimization technique for mapping and localization in robotics. In its latest incarnation, iSAM2 , we represent what we know about the robot’s environment as a large tree data structure called the Bayes tree, which we iteratively update as more measurements come in via the robot’s sensors. Please refer to the publications below, but you can also watch a movie here.
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Related Papers
International Journal of Robotics Research (IJRR)
2012
iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree,
, International Journal of Robotics Research (IJRR), (2012)
ICRA
2011
2010
The Bayes Tree: Enabling Incremental Reordering and Fluid Relinearization for Online Mapping,
, Jan, Number MIT-CSAIL-TR-2010-021, (2010)
WAFR
2010
The Bayes Tree: An Algorithmic Foundation for Probabilistic Robot Mapping,
, WAFR, Dec, (2010)
Transactions on Robotics (TRO)
2008
iSAM: Incremental Smoothing and Mapping,
, Transactions on Robotics (TRO), (2008)
ICRA
2007
iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association,
, ICRA, April, Rome; Italy, (2007)
