Visual based place recognition involves recognising familiar locations despite changes in
environment or view-point of the camera(s) at the locations. There are existing methods
that deal with these seasonal changes or view-point changes separately, but few methods
exist that deal with these kind of changes simultaneously. Such robust place recognition
systems are essential to long term localization and autonomy. Such systems should be
able to deal both with conditional and viewpoint changes simultaneously. In recent times
Convolutional Neural Networks (CNNs) have shown to outperform other state-of-the art
method in task related to classi cation and recognition including place recognition. In this
thesis, we present a deep learning based planar omni-directional place recognition approach
that can deal with conditional and viewpoint variations together. The proposed method
is able to deal with large viewpoint changes, where current methods fail. We evaluate the
proposed method on two real world datasets dealing with four di erent seasons through out
the year along with illumination changes and changes occurred in the environment across
a period of 1 year respectively. We provide both quantitative (recall at 100% precision)
and qualitative (confusion matrices) comparison of the basic pipeline for place recognition
for the omni-directional approach with single-view and side-view camera approaches. The
proposed approach is also shown to work very well across di erent seasons. The results
prove the e cacy of the proposed method over the single-view and side-view cameras
in dealing with conditional and large viewpoint changes in di erent conditions including
illumination, weather, structural changes etc
