  A key functional block of visual navigation system for intelligent autonomous
vehicles is Loop Closure detection and subsequent relocalisation.
State-of-the-Art methods still approach the problem as uni-directional along
the direction of the previous motion. As a result, most of the methods fail in
the absence of a significantly similar overlap of perspectives. In this study,
we propose an approach for bi-directional loop closure. This will, for the
first time, provide us with the capability to relocalize to a location even
when traveling in the opposite direction, thus significantly reducing long-term
odometry drift in the absence of a direct loop. We present a technique to
select training data from large datasets in order to make them usable for the
bi-directional problem. The data is used to train and validate two different
CNN architectures for loop closure detection and subsequent regression of 6-DOF
camera pose between the views in an end-to-end manner. The outcome packs a
considerable impact and aids significantly to real-world scenarios that do not
offer direct loop closure opportunities. We provide a rigorous empirical
comparison against other established approaches and evaluate our method on both
outdoor and indoor data from the FinnForest dataset and PennCOSYVIO dataset.
