HERO-SLAM: Hybrid Enhanced Robust Optimization of Neural SLAM

Simultaneous Localization and Mapping (SLAM) is a fundamental task in robotics, driving numerous applications such as autonomous driving and virtual reality. Recent progress on neural implicit SLAM has shown encouraging and impressive results. However, the robustness of neural SLAM, particularly in...

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Bibliographic Details
Published inarXiv.org
Main Authors Xin, Zhe, Yue, Yufeng, Zhang, Liangjun, Wu, Chenming
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 26.07.2024
Subjects
Data collection
Optimization
Robotics
Robustness
Simultaneous localization and mapping
Virtual reality
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Summary:Simultaneous Localization and Mapping (SLAM) is a fundamental task in robotics, driving numerous applications such as autonomous driving and virtual reality. Recent progress on neural implicit SLAM has shown encouraging and impressive results. However, the robustness of neural SLAM, particularly in challenging or data-limited situations, remains an unresolved issue. This paper presents HERO-SLAM, a Hybrid Enhanced Robust Optimization method for neural SLAM, which combines the benefits of neural implicit field and feature-metric optimization. This hybrid method optimizes a multi-resolution implicit field and enhances robustness in challenging environments with sudden viewpoint changes or sparse data collection. Our comprehensive experimental results on benchmarking datasets validate the effectiveness of our hybrid approach, demonstrating its superior performance over existing implicit field-based methods in challenging scenarios. HERO-SLAM provides a new pathway to enhance the stability, performance, and applicability of neural SLAM in real-world scenarios. Code is available on the project page: https://hero-slam.github.io.
ISSN:2331-8422