  Significant advances have been made recently in Visual Place Recognition
(VPR), feature correspondence, and localization due to the proliferation of
deep-learning-based methods. However, existing approaches tend to address,
partially or fully, only one of two key challenges: viewpoint change and
perceptual aliasing. In this paper, we present novel research that
simultaneously addresses both challenges by combining deep-learned features
with geometric transformations based on reasonable domain assumptions about
navigation on a ground-plane, whilst also removing the requirement for
specialized hardware setup (e.g. lighting, downwards facing cameras). In
particular, our integration of VPR with SLAM by leveraging the robustness of
deep-learned features and our homography-based extreme viewpoint invariance
significantly boosts the performance of VPR, feature correspondence, and pose
graph submodules of the SLAM pipeline. For the first time, we demonstrate a
localization system capable of state-of-the-art performance despite perceptual
aliasing and extreme 180-degree-rotated viewpoint change in a range of
real-world and simulated experiments. Our system is able to achieve early loop
closures that prevent significant drifts in SLAM trajectories. We also compare
extensively several deep architectures for VPR and descriptor matching. We also
show that superior place recognition and descriptor matching across opposite
views results in a similar performance gain in back-end pose graph
optimization.
