用单次全景激光雷达扫描，实现未知环境行人动态的通用建图。
EgoNeMo: Transferable Map of Pedestrian Dynamics via Egocentric LiDAR Scan
- 基于连续隐式建模，仅凭全景激光雷达点云构建通用动态地图。
- 在稀疏数据下仍能从单次扫描重建未知环境的行人运动分布。
- 适合需要快速适应新场景的机器人导航与轨迹预测任务。
本文提出一种可迁移的动态地图（MoD）框架，仅使用全景3D激光雷达点云即可泛化至未知环境，突破传统方法依赖特定地点大量轨迹数据的局限。传统MoD方法需在每个新位置重新采集轨迹，而本方法借助神经隐式建模，在多样环境中训练连续的激光雷达驱动型动态地图估计器。为缓解真实轨迹数据的稀疏性与时间偏差问题，引入位置均衡采样策略和多任务学习架构，联合预测运动分布与空间频率得分图；后者通过可见性感知损失进一步增强，以弥补观测不全的问题。大量实验表明，即使训练数据稀疏，该方法也能仅凭一次瞬时激光雷达扫描，有效重建未知位置的底层运动地图。最终验证其显著提升了下游轨迹预测的可靠性。
原文摘要 · Abstract (English)
This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments using only egocentric 3D LiDAR point clouds to overcome the long-standing limitation of traditional MoD methods. While MoDs are essential for encoding human motion characteristics to enable accurate pedestrian trajectory prediction or safe robot navigation, traditional approaches suffer from site-specificity, requiring exhaustive trajectory accumulation at every new location. Extending recent advances in neural implicit modeling, our framework trains a continuous, LiDAR-based MoD estimator across diverse environments. To mitigate the inherent sparsity and temporal bias of real-world trajectory data, we introduce a position-balanced sampling strategy and a multi-task learning architecture that jointly predicts motion distributions and a spatial frequency score map. The latter is further augmented by visibility-aware losses to compensate for incomplete observation data. Comprehensive experiments demonstrate that our method effectively reconstructs underlying motion maps even in unknown locations from a single instantaneous LiDAR scan, despite highly sparse training data. Finally, we show that our improvements enhance the reliability of downstream trajectory prediction.
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