iSAM: Incremental Smoothing and Mapping

By Michael Kaess, Ananth Ranganathan, Frank Dellaert. 2008

1,197 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

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Citations in the corpus

The limits and potentials of deep learning for robotics (2018, methodology): “This would allow robots to treat a deep neural network like any other sensor, and use the established Bayesian techniques (117; 58; 53) to fuse the network’s predictions with prior knowledge or other sensor measurements, or to accumulate information over time.”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, positive): “Most SLAM algorithms are based on iterative nonlinear optimization [64], [100], [125], [126], [192], [204].”
Learning spatial-semantic representations from natural language descriptions and scene classifications (2014, methodology): “We use the iSAM algorithm [18], which iteratively solves for the QR factorization of the information matrix.”
CubeSLAM: Monocular 3-D Object SLAM (2018, methodology): “Then the optimization problem can be solved by Gauss–Newton or Levenberg–Marquardt algorithm available in many libraries, such as g2o [37] and iSAM [38].”
PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments (2017, neutral): “, [24] and [42]).”
Incremental smoothing and mapping (2008, methodology): “We have since published an extended article [81] that presents parts of this chapter.”
Visual-Inertial Navigation: A Concise Review (2019, methodology): “[27] employed the factor graph to represent the VINS problem and then solved it incrementally in analogy to iSAM [81, 82].”
Kintinuous: Spatially Extended KinectFusion (2012, background): “Shown is the cumulative generated mesh as viewed from the camera estimate and a depth map synthesized from this render. have also experimented with integrating our pose-graph with iSAM [8] for smoothing and global pose-graph optimisation.”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “A few recently proposed algorithms of this type are TreeMap [153], TORO [154], iSAM [155], iSAM2 [156], and g2o [157].”
State Estimation for Robotics (2017, methodology): “Kaess et al. (2008, 2012) provide incremental methods of updating the batch-SLAM solution that exploit sparsity beyond the primary block structure discussed here.”
Learning models for following natural language directions in unknown environments (2015, background): “The factorization within the last line models the metric map induced by the topology, as with pose graph representations [17].”
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry (2015, methodology): “A breakthrough has been the development of incremental smoothing techniques (iSAM [36], iSAM2 [3]), which leverage the expressiveness of factor graphs to maintain sparsity and to identify and update only the typically small subset of variables affected by a new measurement.”
Integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds (2011, methodology): “The sphere400 dataset [19] generated by a virtual robot driving on a 3D sphere.”
A constant-time SLAM back-end in the continuum between global mapping and submapping: application to visual stereo SLAM (2016, methodology): “Another notable approach to SLAM is incremental Smoothing And Mapping (iSAM) (Kaess et al. 2008), where a factored representation of the approximate Hessian matrix is exploited to provide easy access to the marginal covariances needed for data association.”
Learning Semantic Maps from Natural Language Descriptions (2013, methodology): “This model resembles the pose graph representation commonly employed by SLAM solutions [22].”
Factor Graphs and GTSAM: A Hands-on Introduction (2012, positive): “The much larger conservative covariance estimates (green, large ellipses) were based on our earlier work in (Kaess et al., 2008).”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, positive): “Mapping) [81], [82] simplifies the information matrix to speed up the underlying optimization.”