Probabilistic Appearance Based Navigation and Loop Closing
- 1 April 2007
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 10504729,p. 2042-2048
- https://doi.org/10.1109/robot.2007.363622
Abstract
This paper describes a probabilistic framework for navigation using only appearance data. By learning a generative model of appearance, we can compute not only the similarity of two observations, but also the probability that they originate from the same location, and hence compute a pdf over observer location. We do not limit ourselves to the kidnapped robot problem (localizing in a known map), but admit the possibility that observations may come from previously unvisited places. The principled probabilistic approach we develop allows us to explicitly account for the perceptual aliasing in the environment - identical but indistinctive observations receive a low probability of having come from the same place. Our algorithm complexity is linear in the number of places, and is particularly suitable for online loop closure detection in mobile robotics.Keywords
This publication has 12 references indexed in Scilit:
- Engineering an External Memory Minimum Spanning Tree AlgorithmPublished by Springer Nature ,2006
- Outdoor SLAM using visual appearance and laser rangingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Supervised Learning of Places from Range Data using AdaBoostPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Visual odometry and map correlationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Scale & Affine Invariant Interest Point DetectorsInternational Journal of Computer Vision, 2004
- Recognizing Objects in Range Data Using Regional Point DescriptorsPublished by Springer Nature ,2004
- Incremental mapping of large cyclic environmentsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Video Google: a text retrieval approach to object matching in videosPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Object recognition from local scale-invariant featuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1999
- Approximating discrete probability distributions with dependence treesIEEE Transactions on Information Theory, 1968