Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-Aliased Indoor Environments

Understand

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).