提出FreeDOM框架，实时移除动态物体，提升静态地图构建精度。
FreeDOM: Online Dynamic Object Removal Framework for Static Map Construction Based on Conservative Free Space Estimation
- 基于保守空闲空间估计，分前后端处理激光扫描数据。
- 在多个数据集上平均F1分数提升9.7%，优于现有方法。
- 适合需要高精度地图的自动驾驶与机器人导航场景。
在线地图构建对自主机器人在未知环境中的导航至关重要。然而，动态物体的存在可能在地图中引入伪影，显著降低定位与路径规划性能。为此，本文提出一种基于保守空闲空间估计的在线动态物体移除框架FreeDOM，包含扫描移除前段和地图优化后段。首先设计多分辨率地图结构以实现快速计算与高效表示；在扫描移除前段，采用射线投射增强技术提升空闲空间估计，并基于估计结果分割激光扫描；在地图优化后段，利用增量空闲空间信息进一步消除地图中残留的动态物体。在SemanticKITTI、HeLiMOS及多个室内数据集（含多种传感器）上的实验验证表明，该框架克服了基于可见性的方法局限，相比当前最优方法平均F1分数提升9.7%。
原文摘要 · Abstract (English)
Online map construction is essential for autonomous robots to navigate in unknown environments. However, the presence of dynamic objects may introduce artifacts into the map, which can significantly degrade the performance of localization and path planning. To tackle this problem, a novel online dynamic object removal framework for static map construction based on conservative free space estimation (FreeDOM) is proposed, consisting of a scan-removal front-end and a map-refinement back-end. First, we propose a multi-resolution map structure for fast computation and effective map representation. In the scan-removal front-end, we employ raycast enhancement to improve free space estimation and segment the LiDAR scan based on the estimated free space. In the map-refinement back-end, we further eliminate residual dynamic objects in the map by leveraging incremental free space information. As experimentally verified on SemanticKITTI, HeLiMOS, and indoor datasets with various sensors, our proposed framework overcomes the limitations of visibility-based methods and outperforms state-of-the-art methods with an average F1-score improvement of 9.7%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。