Fast iterative alignment of pose graphs with poor initial estimates

By Edwin Olson, John J. Leonard, Seth J. Teller. Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., 2006

512 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0f7bbb34d0ab256979782687e561edb4b77489a9

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

On measuring the accuracy of SLAM algorithms (2009, background): “…al., 1990], sparse extended information filters [Eustice et al., 2005a; Thrun et al., 2004], particle filters [Montemerlo et al., 2003; Grisetti et al., 2007b], and least square error minimization approaches [Lu and Milios, 1997; Frese et al., 2005; Gutmann and Konolige, 1999; Olson et al., 2006].”
iSAM2: Incremental smoothing and mapping using the Bayes tree (2012, background): “(Olson et al., 2006).”
An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics (2015, background): “SLAM has been extensively studied in the past couple of decades [48,66,91] resulting in many different solutions using different sensors, including sonar sensors [71], IR sensors [1] and LASER scanners [26].”
Robust map optimization using dynamic covariance scaling (2013, methodology): “The required pptimization time shows a larger variance for random outliers in ManhattanOlson compared to ManhattanG2O as the former starts with a worse initialization.”
Visual simultaneous localization and mapping: a survey (2012, unknown): “However, it is necessary to perform a global optimization of the map to reduce local error (Frese et al. 2005; Olson et al. 2006).”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, background): “, 2005), iterative alignment (Olson et al., 2006), square root simultaneous location and mapping (SAM) (Frank and Michael, 2006), incremental smoothing and mapping (iSAM) (Kaess et al.”
Globally consistent 3D mapping with scan matching (2008, methodology): “Without considering any sensor data such as laser scans, Olson et al. create globally consistent maps by minimizing the global non-linear constraint network on a set of poses [24].”
AUV Navigation and Localization: A Review (2014, methodology): “Different solutions exist for GrpahSLAM, such as relaxation on a mesh [41], multilevel relaxation [42], iterative alignment [43], square root smoothing and mapping (SAM) [7], incremental smoothing and mapping (iSAM) [44] and works by Grisetti et al.”
Scale Drift-Aware Large Scale Monocular SLAM (2010, background): “One solution would be to optimise over relative constraints between poses using pose-graph optimisation [12][23].”