用多分辨率子地图优化，让神经隐式建图更快更准。
MISO: Multiresolution Submap Optimization for Efficient Globally Consistent Neural Implicit Reconstruction
- 分块处理环境，用分层优化和学习初始化加速局部建图
- 多分辨率对齐融合，避免解码全场景几何，提速显著
- 适合大场景实时建图，提升精度与计算效率
神经隐式表示在同时定位与建图（SLAM）中带来重大影响，使机器人能从传感器数据构建连续、可微且高保真的3D地图。然而，随着环境规模与复杂度增加，神经SLAM在后端优化中面临新的挑战，难以满足实时性要求并保持全局一致性。本文提出MISO，一种基于多分辨率子地图的分层优化方法，实现高效可扩展的神经隐式重建。针对每个子地图内的局部SLAM，设计了带有学习初始化的分层优化方案，大幅缩短隐式子地图特征优化时间。为全局纠正估计漂移，提出分层对齐与融合多分辨率子地图的方法，显著加速过程，无需解码完整场景几何。MISO在大规模真实世界基准测试中显著提升了神经符号距离函数（SDF）SLAM的计算效率与估计精度。
原文摘要 · Abstract (English)
Neural implicit representations have had a significant impact on simultaneous localization and mapping (SLAM) by enabling robots to build continuous, differentiable, and high-fidelity 3D maps from sensor data. However, as the scale and complexity of the environment increase, neural SLAM approaches face renewed challenges in the back-end optimization process to keep up with runtime requirements and maintain global consistency. We introduce MISO, a hierarchical optimization approach that leverages multiresolution submaps to achieve efficient and scalable neural implicit reconstruction. For local SLAM within each submap, we develop a hierarchical optimization scheme with learned initialization that substantially reduces the time needed to optimize the implicit submap features. To correct estimation drift globally, we develop a hierarchical method to align and fuse the multiresolution submaps, leading to substantial acceleration by avoiding the need to decode the full scene geometry. MISO significantly improves computational efficiency and estimation accuracy of neural signed distance function (SDF) SLAM on large-scale real-world benchmarks.
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