Fast covariance recovery in incremental nonlinear least square solvers
Many estimation problems in robotics rely on efficiently solving nonlinear least squares (NLS). For example, it is well known that the simultaneous localisation and mapping (SLAM) problem can be formulated as a maximum likelihood estimation (MLE) and solved using NLS, yielding a mean state vector. However, for many applications recovering only the mean vector is not enough. Data association, active decisions, next best view, are only few of the applications that require fast state covariance...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings - IEEE International Conference on Robotics and Automation|
|01_Ila_Fast_covariance_recovery_in_2015.pdf||1.59 MB||Adobe PDF||Request a copy|
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