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.

翻译：同步定位与建图（SLAM）是机器人学中的一项基础任务，推动了自动驾驶、虚拟现实等众多应用的发展。近期，神经隐式SLAM的研究取得了令人鼓舞且瞩目的进展。然而，神经SLAM的鲁棒性，尤其是在具有挑战性或数据受限的场景中，仍然是一个尚未解决的问题。本文提出了HERO-SLAM，一种用于神经SLAM的混合增强鲁棒优化方法，它结合了神经隐式场与特征度量优化的优势。该混合方法优化了一个多分辨率隐式场，并在视角突变或数据采集稀疏的挑战性环境中增强了鲁棒性。我们在基准数据集上的全面实验结果验证了该混合方法的有效性，表明其在挑战性场景中优于现有的基于隐式场的方法。HERO-SLAM为提升神经SLAM在真实世界场景中的稳定性、性能与适用性提供了一条新途径。代码已在项目页面发布：https://hero-slam.github.io。