The Extended Kalman Filter (EKF)

Investigating using the EKF 

The Extended Kalman Filter (EKF) has received abundant attention with the growing demands for robotic localization. The EKF algorithm is more realistic in non-linear systems, which has an autonomous white noise in both the system and the estimation model. Also, in the field of engineering, most systems are non-linear.

 Reference video description :

 This video composes three synchronized views at the beginning and also shows the 3D reconstruction
of the structure at the end. The top left corner shows the real operation of the robot moving around the
structure gathering the 3D scans. The bottom left corner shows the generated map with each scan
painted with a different color. There it can be seen how the point clouds are added to the map with
the estimation of the robot position (i.e. with the pose predicted by the EKF) producing inconsistencies
and that, after the registration results are incorporated to the EKF the map consistency improves.
The right side of the video shows the online 3D visualizer where the generated map appears coloured
according to the depth and the online 3D laser scans appear in white.
The scans used for the SLAM are presented with the keypoints (the ones used for feature description)
highlighted in black. Note how the different elements that we have described in this section appear in
the video. In this way, see how the current scan diverges from the gathered map when the robot moves
fast, specially when it oscillates in roll as a consequence of the lateral motion of the robot but, it is
minimized when the robot is kept in position. Moreover, note how the current scan inconsistency with
respect to the map is bigger when the DVL has some beams hitting the right hand side
slope (i.e. losing bottom track) and how this drift is corrected after the registrations are incorporated in the EKF.

This approach will work best in improving the current drill visualization system that uses the traditional acoustic sonar