Elements of information theory
Elements of information theory
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Fast, On-Line Learning of Globally Consistent Maps
Autonomous Robots
Monte carlo em for data-association and its applications in computer vision
Monte carlo em for data-association and its applications in computer vision
Active Search for Real-Time Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Algorithm 849: A concise sparse Cholesky factorization package
ACM Transactions on Mathematical Software (TOMS)
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
iSAM: Incremental Smoothing and Mapping
IEEE Transactions on Robotics
Probabilistic structure matching for visual SLAM with a multi-camera rig
Computer Vision and Image Understanding
Distributed consensus algorithms for merging feature-based maps with limited communication
Robotics and Autonomous Systems
iSAM2: Incremental smoothing and mapping using the Bayes tree
International Journal of Robotics Research
Information-theoretic compression of pose graphs for laser-based SLAM
International Journal of Robotics Research
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Data association is one of the core problems of simultaneous localization and mapping (SLAM), and it requires knowledge about the uncertainties of the estimation problem in the form of marginal covariances. However, it is often difficult to access these quantities without calculating the full and dense covariance matrix, which is prohibitively expensive. We present a dynamic programming algorithm for efficient recovery of the marginal covariances needed for data association. As input we use a square root information matrix as maintained by our incremental smoothing and mapping (iSAM) algorithm. The contributions beyond our previous work are an improved algorithm for recovering the marginal covariances and a more thorough treatment of data association, now including the joint compatibility branch and bound (JCBB) algorithm. We further show how to make information theoretic decisions about measurements before actually taking the measurement, therefore allowing a reduction in estimation complexity by omitting uninformative measurements. We evaluate our work on simulated and real-world data.