In robotic mapping and navigation, simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
While this initially appears to be a chicken-and-egg problem there are several algorithms known for solving it, at least approximately, in tractable time for certain environments.
A multiple template method to track fast motion target with appearance changes is presented under the framework of appearance model with Kalman filter.
Firstly, we construct a multiple template appearance model, which includes both the original template and templates affinely transformed from original one.
The experiments show that the new algorithm is superior to those based on Kalman filters.
Dynamic speech properties, such as time warping, silence removal and background noise reduction are the most challenging issues in continuous speech signal matching.
Among all of them, the time warped speech signal matching is of great interest and has been a tough challenge for the researchers.
Simultaneously, another position observation is produced by a feature based distance metric.
The position estimated by the state model is fused with the observation using KF along with the noise variances.