A stochastic map for uncertain spatial relationships
Proceedings of the 4th international symposium on Robotics Research
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
Visually Mapping the RMS Titanic: Conservative Covariance Estimates for SLAM Information Filters
International Journal of Robotics Research
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
Recognizing places using spectrally clustered local matches
Robotics and Autonomous Systems
Real-time correlative scan matching
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Inference in hybrid networks: theoretical limits and practical algorithms
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
iSAM: Incremental Smoothing and Mapping
IEEE Transactions on Robotics
Progress toward multi-robot reconnaissance and the MAGIC 2010 competition
Journal of Field Robotics
Simultaneous localization and mapping with multimodal probability distributions
International Journal of Robotics Research
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The central challenge in robotic mapping is obtaining reliable data associations (or "loop closures"): state-of-the-art inference algorithms can fail catastrophically if even one erroneous loop closure is incorporated into the map. Consequently, much work has been done to push error rates closer to zero. However, a long-lived or multi-robot system will still encounter errors, leading to system failure. We propose a fundamentally different approach: allow richer error models that allow the probability of a failure to be explicitly modeled. In other words, rather than characterizing loop closures as being "right" or "wrong", we propose characterizing the error of those loop closures in a more expressive manner that can account for their non-Gaussian behavior. Our approach leads to an fully integrated Bayesian framework for dealing with error-prone data. Unlike earlier multiple-hypothesis approaches, our approach avoids exponential memory complexity and is fast enough for real-time performance. We show that the proposed method not only allows loop closing errors to be automatically identified, but also that in extreme cases, the "front-end" loop-validation systems can be unnecessary. We demonstrate our system both on standard benchmarks and on the real-world data sets that motivated this work.