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Automatic Relocalization and Loop Closing for Real-Time Monocular SLAM
September 2011 (vol. 33 no. 9)
pp. 1699-1712
ASCII Text
x 
 
Brian Williams, Georg Klein, Ian Reid, "Automatic Relocalization and Loop Closing for Real-Time Monocular SLAM," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 9, pp. 1699-1712, September, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2011.41,
author = {Brian Williams and Georg Klein and Ian Reid},
title = {Automatic Relocalization and Loop Closing for Real-Time Monocular SLAM},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {33},
number = {9},
issn = {0162-8828},
year = {2011},
pages = {1699-1712},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.41},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Automatic Relocalization and Loop Closing for Real-Time Monocular SLAM
IS - 9
SN - 0162-8828
SP1699
EP1712
EPD - 1699-1712
A1 - Brian Williams,
A1 - Georg Klein,
A1 - Ian Reid,
PY - 2011
KW - Tracking
KW - 3D/stereo scene analysis
KW - autonomous vehicles.
VL - 33
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Brian Williams, Jet Propulsion Laboratory, Pasadena
Georg Klein, Microsoft Corporation, Seattle
Ian Reid, University of Oxford, Oxford
Monocular SLAM has the potential to turn inexpensive cameras into powerful pose sensors for applications such as robotics and augmented reality. We present a relocalization module for such systems which solves some of the problems encountered by previous monocular SLAM systems—tracking failure, map merging, and loop closure detection. This module extends recent advances in keypoint recognition to determine the camera pose relative to the landmarks within a single frame time of 33 ms. We first show how this module can be used to improve the robustness of these systems. Blur, sudden motion, and occlusion can all cause tracking to fail, leading to a corrupted map. Using the relocalization module, the system can automatically detect and recover from tracking failure while preserving map integrity. Extensive tests show that the system can then reliably generate maps for long sequences even in the presence of frequent tracking failure. We then show that the relocalization module can be used to recognize overlap in maps, i.e., when the camera has returned to a previously mapped area. Having established an overlap, we determine the relative pose of the maps using trajectory alignment so that independent maps can be merged and loop closure events can be recognized. The system combining all of these abilities is able to map larger environments and for significantly longer periods than previous systems.

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Index Terms:
Tracking, 3D/stereo scene analysis, autonomous vehicles.
Citation:
Brian Williams, Georg Klein, Ian Reid, "Automatic Relocalization and Loop Closing for Real-Time Monocular SLAM," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 9, pp. 1699-1712, Sept. 2011, doi:10.1109/TPAMI.2011.41
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