Vision-based Mapping with Backward Correction
We consider the problem of creating a consistent alignment of multiple 3D submaps
containing distinctive visual landmarks in an unmodified environment.
An efficient map alignment algorithm based on landmark specificity
is proposed to align submaps. This is followed by a global minimization using
the close-the-loop constraint. Landmark uncertainty is taken into account
in the pair-wise alignment and the global minimization process.
Experiments show that pair-wise alignment of submaps with
backward correction produces a consistent global 3D map.
Our vision-based mapping approach using sparse 3D data is different from other
existing approaches which use dense 2D range data from laser or sonar