Algorithms for Simultaneous Localization and Mapping

@inproceedings{Chen2013AlgorithmsFS,
  title={Algorithms for Simultaneous Localization and Mapping},
  author={Yuncong Chen},
  year={2013}
}
Simultaneous Localization and Mapping (SLAM) is the problem in which a sensor-enabled mobile robot incrementally builds a map for an unknown environment, while localizing itself within this map. Efficient and accurate SLAM is fundamental for any mobile robot to perform robust navigation. It is also the cornerstone for higher-level tasks such as path planning and exploration. In this talk, I will survey the three major families of SLAM algorithms: parametric filter, particle filter and graph… CONTINUE READING

Citations

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A Review : Simultaneous Localization and Mapping in Application to Autonomous Robot

Agunbiade, Zuva T Vaal
2018
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Review. Classification and Comparison of the Existing SLAM Methods for Groups of Robots

2018 22nd Conference of Open Innovations Association (FRUCT) • 2018
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References

Publications referenced by this paper.
Showing 1-10 of 55 references

Divide and Conquer : EKF SLAM in O ( n )

View 3 Excerpts
Highly Influenced

iSAM: Incremental Smoothing and Mapping

IEEE Transactions on Robotics • 2008
View 4 Excerpts
Highly Influenced

Dp-slam 2.0

IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 • 2004
View 4 Excerpts
Highly Influenced

G2o: A general framework for graph optimization

2011 IEEE International Conference on Robotics and Automation • 2011
View 2 Excerpts

Efficient Sparse Pose Adjustment for 2D mapping

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems • 2010
View 2 Excerpts

Multi-level submap based SLAM using nested dissection

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems • 2010
View 3 Excerpts

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