eNavi：基于事件的模仿策略用于低光室内移动机器人导航
eNavi: Event-based Imitation Policies for Low-Light Indoor Mobile Robot Navigation
AI总结：
本文提出eNavi，通过结合RGB和事件数据的多模态策略提升低光环境下机器人导航的鲁棒性，实验显示融合模型在未见低光条件下表现更优。
摘要：
事件相机提供高动态范围和微秒级时间分辨率，使其适合室内机器人导航，传统RGB相机在快速运动或低光条件下性能下降。尽管事件感知在检测、SLAM和姿态估计方面有进展，但端到端控制策略利用事件流异步特性仍有不足。为此，我们引入了一个真实室内人跟随数据集，使用TurtleBot 2机器人收集，包含同步的原始事件流、RGB帧和专家控制动作。我们构建了多模态数据预处理管道，对齐事件和RGB观测并重建地面真实动作以支持高质量模仿学习。基于此数据集，我们提出了一种晚融合的RGB-Event导航策略，结合双MobileNet编码器和基于Transformer的融合模块，通过行为克隆训练。系统评估了RGB-only、Event-only和RGB-Event融合模型在12种训练变体中的性能，结果显示融合模型在未见低光条件下鲁棒性更高，动作预测误差更低。
英文摘要：
Event cameras provide high dynamic range and microsecond-level temporal resolution, making them well-suited for indoor robot navigation, where conventional RGB cameras degrade under fast motion or low-light conditions. Despite advances in event-based perception spanning detection, SLAM, and pose estimation, there remains limited research on end-to-end control policies that exploit the asynchronous nature of event streams. To address this gap, we introduce a real-world indoor person-following dataset collected using a TurtleBot 2 robot, featuring synchronized raw event streams, RGB frames, and expert control actions across multiple indoor maps, trajectories under both normal and low-light conditions. We further build a multimodal data preprocessing pipeline that temporally aligns event and RGB observations while reconstructing ground-truth actions from odometry to support high-quality imitation learning. Building on this dataset, we propose a late-fusion RGB-Event navigation policy that combines dual MobileNet encoders with a transformer-based fusion module trained via behavioral cloning. A systematic evaluation of RGB-only, Event-only, and RGB-Event fusion models across 12 training variations ranging from single-path imitation to general multi-path imitation shows that policies incorporating event data, particularly the fusion model, achieve improved robustness and lower action prediction error, especially in unseen low-light conditions where RGB-only models fail. We release the dataset, synchronization pipeline, and trained models at https://eventbasedvision.github.io/eNavi/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
