1) Description:
This video clearly illustrates the inverse depth - XYZ coding conversion in monocular EKF SLAM with a real image loop-closing sequence experiment. The sequence was recorded in a computer room at Imperial College (London) with a hand-held 90ºfov 320x240 Unibrain camera, at 30 frames per second.

The left figure in the video displays the image sequence and the point features. Features are plotted as a star if they are coded in inverse depth, and as a triangle if they have switched to XYZ parameterization. 

The right figure shows a top view of the 3D estimated map and its 95% uncertainty regions. Inverse depth features are drawn as stars, XYZ ones as triangles. Features and their covariance are plotted in red. Camera estimated location, uncertainty and trajectory are plotted in blue.

As it is detailed in the paper, inverse depth coding is mandatory at initialization, when we have to code the infinity uncertainty in the ray. It can be noticed in the 3D estimated map that XYZ uncertainty regions of newly initialized features (inverse depth features) are far from the gaussianity desired by the EKF. As a result of this, if an early switch would have been done, performance of the filter would have been degraded.

Notice also how, as the sequence evolves, some features are seen with parallax enough to have a depth estimation, and their covariance become Gaussian also in XYZ (it continues being Gaussian in inverse depth). These are the features that can be switched to XYZ parameterization. The conversion is done according to an index that measures the linearity of the XYZ parameterization and a threshold. Both, index and threshold are theoretically discussed in the paper. 

It is important to remark here that the conversion is done only in order to improve the computational efficiency of the Extended Kalman Filter. Same performance is achieved if all features remain in inverse depth parameterization, as inverse depth parameterization is Gaussian for high-parallax and low-parallax features. This fact is discussed in the paper.

Notice, finally, that some features are never seen with parallax enough in this sequence (the further ones and the recently initialized ones). So, not all features can be converted to Euclidean parameterization (and neither at any time, as it is said in fourth and fifth paragraphs).


2) Multimedia player information:
Codec: ffdshow MPEG-4 Video Decoder.


3) Author contact information:
Javier Civera, jcivera@unizar.es, University of Zaragoza (Zaragoza, Spain).
Andrew J. Davison, ajd@doc.ic.ac.uk, Imperial College (London, UK).
J. M. M. Montiel, josemari@unizar.es, University of Zaragoza (Zaragoza, Spain).


Attachment filename: 1146_VI.mp4 (4773509 Bytes)