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This paper presents a robust approach for a visual parallel tracking andmapping (PTAM) system that excels in challenging environments. Our proposedmethod combines the strengths of heterogeneous multi-modal visual sensors,including stereo event-based and frame-based sensors, in a unified referenceframe through a novel spatio-temporal synchronization of stereo visual framesand stereo event streams. We employ deep learning-based feature extraction anddescription for estimation to enhance robustness further. We also introduce anend-to-end parallel tracking and mapping optimization layer complemented by asimple loop-closure algorithm for efficient SLAM behavior. Throughcomprehensive experiments on both small-scale and large-scale real-worldsequences of VECtor and TUM-VIE benchmarks, our proposed method (DH-PTAM)demonstrates superior performance in terms of robustness and accuracy inadverse conditions, especially in large-scale HDR scenarios. Ourimplementation's research-based Python API is publicly available on GitHub forfurther research and development: https://github.com/AbanobSoliman/DH-PTAM.

DH-PTAM: A Deep Hybrid Stereo Events-Frames Parallel Tracking And Mapping System