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Robust feature matching forms the backbone for most Visual SimultaneousLocalization and Mapping (vSLAM), visual odometry, 3D reconstruction, andStructure from Motion (SfM) algorithms. However, recovering feature matchesfrom texture-poor scenes is a major challenge and still remains an open area ofresearch. In this paper, we present a Stereo Visual Odometry (StereoVO)technique based on point and line features which uses a novel feature-matchingmechanism based on an Attention Graph Neural Network that is designed toperform well even under adverse weather conditions such as fog, haze, rain, andsnow, and dynamic lighting conditions such as nighttime illumination and glarescenarios. We perform experiments on multiple real and synthetic datasets tovalidate the ability of our method to perform StereoVO under low visibilityweather and lighting conditions through robust point and line matches. Theresults demonstrate that our method achieves more line feature matches thanstate-of-the-art line matching algorithms, which when complemented with pointfeature matches perform consistently well in adverse weather and dynamiclighting conditions.

Stereo Visual Odometry with Deep Learning-Based Point and Line Feature Matching using an Attention Graph Neural Network