提出高度区间过滤方法，高效去除动态点云残留，实现实时高精度地图构建。
HIF: Height Interval Filtering for Efficient Dynamic Points Removal
- 基于柱状高度区间建模，用贝叶斯更新概率实现快速动态点过滤。
- 在公开数据集上速度提升7.7倍，精度接近当前最优方法。
- 适合需要实时处理的自动驾驶与机器人导航场景。
3D点云地图在定位与自主导航中起关键作用，但动态物体常在建图过程中留下残余痕迹，影响后续任务性能。因此，动态物体去除成为动态环境下基于点云的地图构建中的关键挑战。现有方法通常计算开销大，难以满足实时性要求。为此，本文提出高度区间过滤（HIF）方法，通过构建基于柱状的高度区间表示，概率化建模垂直维度，并利用贝叶斯推断更新区间概率，实现高精度与强鲁棒性的同时保证实时性能。此外，提出低高度保留策略，增强对未知区域的探测能力，减少障碍物遮挡区域的误分类。在公开数据集上的实验表明，HIF在时间效率上相比现有最先进方法提升7.7倍，同时保持相近精度。代码将公开共享。
原文摘要 · Abstract (English)
3D point cloud mapping plays a essential role in localization and autonomous navigation. However, dynamic objects often leave residual traces during the map construction process, which undermine the performance of subsequent tasks. Therefore, dynamic object removal has become a critical challenge in point cloud based map construction within dynamic scenarios. Existing approaches, however, often incur significant computational overhead, making it difficult to meet the real-time processing requirements. To address this issue, we introduce the Height Interval Filtering (HIF) method. This approach constructs pillar-based height interval representations to probabilistically model the vertical dimension, with interval probabilities updated through Bayesian inference. It ensures real-time performance while achieving high accuracy and improving robustness in complex environments. Additionally, we propose a low-height preservation strategy that enhances the detection of unknown spaces, reducing misclassification in areas blocked by obstacles (occluded regions). Experiments on public datasets demonstrate that HIF delivers a 7.7 times improvement in time efficiency with comparable accuracy to existing SOTA methods. The code will be publicly available.
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