LAPS通过主动池化与采样提升神经距离场的增量激光映射稳定性。
LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields
- 基于可靠性主动保留关键历史样本，减少冗余存储。
- 根据不确定性引导采样，重点优化未充分约束区域。
- 在真实与合成数据上显著提升重建完整度，适合实时3D建图应用。
神经距离场为3D几何提供了紧凑连续的表示，适用于增量式激光雷达映射。然而，其在线优化易受灾难性遗忘影响，新观测可能损害先前重建的几何结构。重放训练常用于缓解此问题，但现有方法多依赖被动重放缓冲区和均匀采样，导致内存浪费于冗余观测，且对约束不足区域训练不足。我们提出LAPS，一种增量神经映射的重放管理框架，提升重放保留与分配效率。LAPS结合基于可靠性的主动池化，在有限内存下保留可靠历史样本；以及基于不确定性的主动采样，聚焦优化约束不足区域。在合成与真实世界基准上的实验表明，LAPS持续提升重建完整性，同时保持良好的几何精度。在牛津斯皮尔斯数据集的Blenheim Palace 05序列中，相比PIN-SLAM，召回率提升4.66个百分点，F1分数提升3.79个百分点。代码已开源：https://github.com/dongjae0107/LAPS。
原文摘要 · Abstract (English)
Neural distance fields offer a compact and continuous representation of 3D geometry, making them attractive for incremental LiDAR mapping. However, their online optimization is vulnerable to catastrophic forgetting, where new observations can degrade previously reconstructed geometry. Replay-based training is commonly used to address this issue, but existing methods typically rely on passive replay buffers and uniform sampling, which can waste memory on redundant observations and under-train poorly constrained regions. We propose LAPS, a replay management framework for incremental neural mapping that improves both replay retention and replay allocation during online updates. LAPS combines reliability-based active pooling to retain reliable historical samples under limited memory with uncertainty-guided active sampling to focus optimization on under-constrained regions. Experiments on synthetic and real-world benchmarks show that LAPS consistently improves reconstruction completeness while maintaining competitive geometric accuracy. On Oxford Spires, it improves recall by 4.66 pp and F1-score by 3.79 pp over PIN-SLAM on the Blenheim Palace 05 sequence. We release our open source implementation at: https://github.com/dongjae0107/LAPS.
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