发表机构
Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对移动机器人导航安全，提出MamMA算法，利用占用地图块特征和行人感知状态，基于Mamba模型预测轨迹，在多个数据集上优于现有方法。
AI 中文摘要
许多行人轨迹预测算法已被提出，以提高在人类与机器人共存环境中工作的移动机器人导航的安全性。一些行人轨迹预测算法从俯视图图像中提取行人附近障碍物的信息，以提高轨迹预测的准确性。然而，移动机器人通常使用激光雷达创建局部占用地图，而非俯视图图像。同时，机器人上的视觉传感器提供自我中心视图图像，其中包含机器人附近行人的细粒度行为信息。为了更好地利用激光雷达和车载视觉传感器收集的信息，我们提出了MamMA，一种考虑占用地图和行人感知状态的基于Mamba的行人轨迹预测算法。MamMA将占用地图按块划分，并从每个块中提取障碍物特征以创建地图特征。行人感知状态被划分并考虑，因为一些研究表明感知状态影响行人的感知和速度。此外，提出了一种基于Mamba的模型，根据不同类型的特征预测行人的未来轨迹。在STCrowd、SiT、JRDB、ETH和UCY数据集上的实验表明，MamMA在平均位移误差和最终位移误差方面优于最先进的算法。
英文摘要
Many pedestrian trajectory prediction algorithms have been proposed to improve the safety of navigation for mobile robots working in human-robot coexistence environments. Some pedestrian trajectory prediction algorithms extract information about obstacles near pedestrians from top-down view images to improve the accuracy of trajectory prediction. However, mobile robots typically create local occupancy maps using LiDAR, rather than top-down view images. Meanwhile, the vision sensors on board robots provide egocentric view images, which contain fine-grained behavioral information about the pedestrians near the robot. To better use the information collected by LiDAR and on-board vision sensors, we propose MamMA, a Mamba-based pedestrian trajectory prediction algorithm considering occupancy maps and pedestrian awareness states. MamMA divides the occupancy map by patches and extracts obstacle features from each patch to create map features. Pedestrian awareness states are divided and considered, as some studies show that awareness states affect the perception and speed of pedestrians. Furthermore, a Mamba-based model is proposed to predict the future trajectories of pedestrians based on different types of features. Experiments on the STCrowd, SiT, JRDB, ETH, and UCY datasets show that MamMA achieves better average displacement error and final displacement error than the state-of-the-art algorithms.
CommentsAccepted at the 2026 International Joint Conference on Neural Networks (IJCNN 2026). 7 pages, 5 figures
