Satyajit Tourani∗1 Dhagash Desai∗1 Udit Singh Parihar∗1 Sourav Garg2 Ravi Kiran Sarvadevabhatla3 Michael Milford2 K. Madhava Krishna1
Significant recent advances have been made in Visual Place Recognition (VPR), feature correspondence and localization due to 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-learnt features with geometric transformations based on domain knowledge about navigation on a ground-plane, without specialized hardware (e.g. downwards facing cameras, etc.). In particular, our integration of VPR with SLAM by leveraging the robustness of deep-learnt features and our homography-based extreme viewpoint invariance significantly boosts the performance of VPR, feature correspondence and pose graph sub-modules of the SLAM pipeline. 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.