Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing

By Frank Dellaert, Michael Kaess. Int. J. Robotics Res., 2006

1,047 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/c82fcb3d2e390fc835108f0b923ea4c8d24b917d

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Citations in the corpus

Trajectory Optimization using Reinforcement Learning for Map Exploration (2008, background): “More recent mapping research has focused on building maps with robots that do not have access to global position information (Smith et al. 1990 Lu and Milios 1997 Hähnel et al. 2003 Dellaert and Kaess 2006 Eustice et al. 2005 Thrun et al. 2005 Olson et al. 2006).”
Factor Graphs and GTSAM: A Hands-on Introduction (2012, methodology): “Importantly, because we want to use this factor for nonlinear optimization (see e.g., Dellaert and Kaess 2006 for details), whenever the optional argument H is provided, a Matrix reference, the function should assign the Jacobian of h(q) to it, evaluated at the provided value for q.”
An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics (2015, methodology): “SLAM approaches have been categorized into ﬁltering approaches (such as EKF-SLAM [106] and particle ﬁlter based SLAM [81]) and smoothing approaches (such as GraphSLAM [111], RGB-D SLAM [56], Smoothing and Mapping [30]).”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “They extend their work Dellaert & Kaess (2006) on factorizing the matrix of the nonlinear least-squares problem to an incremental approach that only recalculates entries which change in the matrix.”
A Tutorial on Graph-Based SLAM (2010, methodology): “Recently, Konolige et al. [19] proposed an open-source implementation of a pose-graph method that constructs the linearized system in an efﬁcient way.”
AUV Navigation and Localization: A Review (2014, methodology): “In [35], the approach in [133] is improved using iSAM for smoothing and incorporates range updates from a CNA.”
Differentiable Gaussian Process Motion Planning (2019, methodology): “The trajectory update δ θ i is then computed by solving this linear system, using Cholesky decomposition of the normal equations [3], [21], and the new trajectory θ i +1 is computed using a Gauss-Newton step.”
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry (2015, neutral): “(24)”
FutureMapping: The Computational Structure of Spatial AI Systems (2018, background): “While BA can be interpreted in terms of matrix operations, it is also commonly posed directly as a graph algorithm [11], and is clearly well suited to implementation in a message passing manner on a graph processor.”
Autonomous multi-floor indoor navigation with a computationally constrained MAV (2011, methodology): “Both particle filter-based occupancy grid [10] and feature-based [11] [12] methods perform well in practice.”
SE-Sync: A certifiably correct algorithm for synchronization over the special Euclidean group (2016, background): “This approach has proven particularly attractive in robotics and computer vision applications, as the high computational speed of first- and secondorder smooth nonlinear programming methods (Nocedal and Wright, 2006), together with their ability to exploit the measurement sparsity that typically occ”
iSAM: Incremental Smoothing and Mapping (2008, methodology): “Square root SAM [4], [5] provides an efficient and exact solution based on a batch factorization of the information matrix but does not address how to efficiently access the marginal covariances.”
Incremental smoothing and mapping (2008, methodology): “[19] have been developed that yield good results for the SLAM problem as shown in [29, 32].”