EgoNeMo: Transferable Map of Pedestrian Dynamics via Egocentric LiDAR Scan

2026-09-05

AI Summary

本文提出一种基于自我中心3D LiDAR点云的可迁移动态地图框架EgoNeMo，以解决传统方法需要特定地点轨迹积累的问题，通过平衡采样策略和多任务学习架构提高行人轨迹预测的准确性。

Abstract

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.

Problem

• Egocentric LiDAR
• Map of Dynamics (MoD)
• site-specificity
• trajectory prediction

Innovation

• Transferable Map of Dynamics
• Egocentric 3D LiDAR
• Position-balanced Sampling
• Multi-task Learning Architecture
• Visibility-aware Losses