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arXiv · 2306.01891

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

Abanob Soliman·Fabien Bonardi·Désiré Sidibé·Samia Bouchafa
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Abstract

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

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Abanob Soliman, Fabien Bonardi, Désiré Sidibé, Samia Bouchafa. 2023-06-02. DH-PTAM: A Deep Hybrid Stereo Events-Frames Parallel Tracking And Mapping System. https://doi.org/10.1109/tiv.2024.3412595

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