Robust and efficient robotic mapping

By Edwin Olson. 2008

151 citations recorded by the source corpus.

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Citations in the corpus

Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation (2017, methodology): “Although methods based on geometric reconstruction and mapping have proven effective in a range of navigation and collision avoidance domains [1], [2], [3], they impose considerable computational overhead [4] and”
Autonomous flight in unstructured and unknown indoor environments (2009, methodology): “We must therefore obtain relative position estimates measurements by using either visua l odometry [17] or laser scan-matching [18, 19] algorithms.”
Real-time correlative scan matching (2009, methodology): “Alternatively, heuristic methods can be used to determine which pairs of points are likely to be part of a connected surface, or lines/splines can be extracted from larger sets of points [9], [3], [4].”
On behalf of: Multimedia Archives (2012, unknown): “Ground truth data is used to quantify the quality of the final map using the state squared error (SSE) metric by Olson (2008), see also Appendix B. Section 4 then describes the application of MoG pose graphs in the context of a concrete registration method to generate 3D maps.”
On measuring the accuracy of SLAM algorithms (2009, methodology): “, 2007b], and a maximum likelihood SLAM approach based on the graph formulation [Grisetti et al., 2007c; Olson, 2008].”
Highly scalable appearance-only SLAM - FAB-MAP 2.0 (2009, unknown): “of candidates [6].”
Autonomous multi-floor indoor navigation with a computationally constrained MAV (2011, methodology): “We evaluated several laser-based methods for pose estimation such as exhaustive search [5] and feature-based approaches [6, 7].”
A Tutorial on Graph-Based SLAM (2010, background): “Olson [25], for example, presented a front-end with outlier rejection based on spectral clustering.”