LiDAR-BIND-T：面向机器人应用的改进型时间一致传感器模态转换与融合
LiDAR-BIND-T: Improved and Temporally Consistent Sensor Modality Translation and Fusion for Robotic Applications
AI总结：
本文提出 LiDAR-BIND-T，通过时间嵌入相似度、运动对齐损失和窗口化时间融合增强异构传感器到 LiDAR 的转换，并以新指标验证其提升 SLAM 稳定性与精度。
摘要：
本文扩展了 LiDAR-BIND，这是一种将异构传感器（radar、sonar）绑定到由 LiDAR 定义的潜在空间的模块化多模态融合框架，并加入了显式增强时间一致性的机制。我们提出三项贡献：（i）时间嵌入相似度，用于对齐连续的潜在表示；（ii）运动对齐变换损失，用于匹配预测与真值 LiDAR 之间的位移；（iii）利用专用时间模块进行窗口化时间融合。我们还更新了模型架构，以更好地保留空间结构。在 radar/sonar 到 LiDAR 转换上的评估表明，时间和空间连贯性得到改善，在基于 Cartographer 的 SLAM（同步定位与地图构建）中产生了更低的绝对轨迹误差和更好的占据地图精度。我们提出基于 Fréchet Video Motion Distance（FVMD）和相关峰距离度量的不同指标，为评估 SLAM 性能提供实用的时间质量指标。所提出的时间型 LiDAR-BIND（即 LiDAR-BIND-T）保持了模块化模态融合，同时显著增强了时间稳定性，从而提升了下游 SLAM 的鲁棒性和性能。
英文摘要：
This paper extends LiDAR-BIND, a modular multi-modal fusion framework that binds heterogeneous sensors (radar, sonar) to a LiDAR-defined latent space, with mechanisms that explicitly enforce temporal consistency. We introduce three contributions: (i) temporal embedding similarity that aligns consecutive latent representations, (ii) a motion-aligned transformation loss that matches displacement between predictions and ground truth LiDAR, and (iii) windowed temporal fusion using a specialised temporal module. We further update the model architecture to better preserve spatial structure. Evaluations on radar/sonar-to-LiDAR translation demonstrate improved temporal and spatial coherence, yielding lower absolute trajectory error and better occupancy map accuracy in Cartographer-based SLAM (Simultaneous Localisation and Mapping). We propose different metrics based on the Fréchet Video Motion Distance (FVMD) and a correlation-peak distance metric providing practical temporal quality indicators to evaluate SLAM performance. The proposed temporal LiDAR-BIND, or LiDAR-BIND-T, maintains modular modality fusion while substantially enhancing temporal stability, resulting in improved robustness and performance for downstream SLAM.
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。
