Learning to close the loop from 3D point clouds

@article{Granstrm2010LearningTC,
  title={Learning to close the loop from 3D point clouds},
  author={Karl Granstr{\"o}m and Thomas B. Sch{\"o}n},
  journal={2010 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year={2010},
  pages={2089-2095}
}
This paper presents a new solution to the loop closing problem for 3D point clouds. Loop closing is the problem of detecting the return to a previously visited location, and constitutes an important part of the solution to the Simultaneous Localisation and Mapping (SLAM) problem. It is important to achieve a low level of false alarms, since closing a false loop can have disastrous effects in a SLAM algorithm. In this work, the point clouds are described using features, which efficiently reduces… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 12 CITATIONS

Place recognition in 3D scans using a combination of bag of words and point feature based relative pose estimation

VIEW 5 EXCERPTS
CITES BACKGROUND, METHODS & RESULTS
HIGHLY INFLUENCED

Point-Cloud-Based Place Recognition Using CNN Feature Extraction

VIEW 1 EXCERPT

Learning to close loops from range data

VIEW 2 EXCERPTS
CITES METHODS

References

Publications referenced by this paper.
SHOWING 1-10 OF 26 REFERENCES

Appearance-based loop detection from 3D laser data using the normal distributions transform

VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Learning to detect loop closure from range data

VIEW 7 EXCERPTS

Exactly Sparse Delayed-State Filters for View-Based SLAM

VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Highly scalable appearance-only SLAM - FAB-MAP 2.0

VIEW 2 EXCERPTS

The New College Vision and Laser Data Set

VIEW 1 EXCERPT