NeRF-based SLAM has recently achieved promising results in tracking and reconstruction. However, existing methods face challenges in providing sufficient scene representation, capturing structural information, and maintaining global consistency in scenes emerging significant movement or being forgotten. To this end, we present HS-SLAM to tackle these problems. To enhance scene representation capacity, we propose a hybrid encoding network that combines the complementary strengths of hash-grid, tri-planes, and one-blob, improving the completeness and smoothness of reconstruction. Additionally, we introduce structural supervision by sampling patches of non-local pixels rather than individual rays to better capture the scene structure. To ensure global consistency, we implement an active global bundle adjustment (BA) to eliminate camera drifts and mitigate accumulative errors. Experimental results demonstrate that HS-SLAM outperforms the baselines in tracking and reconstruction accuracy while maintaining the efficiency required for robotics.

翻译：基于NeRF的SLAM方法近期在跟踪与重建任务中取得了显著进展。然而，现有方法在提供充分的场景表示、捕获结构信息，以及在存在显著运动或被遗忘的场景中保持全局一致性方面仍面临挑战。为此，我们提出HS-SLAM以应对这些问题。为增强场景表示能力，我们设计了一种混合编码网络，结合了哈希网格、三平面与单斑块编码的互补优势，从而提升了重建的完整性与平滑度。此外，我们通过采样非局部像素块而非单条光线来引入结构监督，以更好地捕获场景结构。为确保全局一致性，我们实现了主动式全局光束法平差以消除相机漂移并减少累积误差。实验结果表明，HS-SLAM在跟踪与重建精度上均优于基线方法，同时保持了机器人应用所需的运行效率。