Abstract:
Visual place recognition using hand crafted and deep features performs well
in static environments. The dynamic environments with extensive changes
which are very common are however di cult to be recognised. The envi-
ronments may vary in appearance due to many reasons: weather changes,
seasonal changes and changes in lightning conditions Visual place recognition
can be incredibly enhanced if it becomes possible to estimate the appearance
of a speci c scene at a speci c time in view of the appearance of the scene
earlier and learning the way in which appearance vary over time. In this the-
sis, we examined whether worldwide appearance changes in an environment
can be learned adequately to enhance place recognition. We used day night
pairs for training a learned model using cGANs that e ciently approximates
a night scene based on a day scene. We have used binary descriptor based on
color histograms for image matching. The experiments have been done on
three datasets collected from di erent environments. The experimental re-
sults show that the visual place recognition with images approximated by the
trained model outperforms the visual place recognition based on raw images
and currently available state of the art methods.
