提升机器人感知中多传感器融合的时序一致性，减少定位误差。
LiDAR-BIND-T: Improved and Temporally Consistent Sensor Modality Translation and Fusion for Robotic Applications
- 引入时序嵌入相似性与运动对齐损失，保持连续帧间特征一致。
- 在Cartographer SLAM中实现更低轨迹误差和更高占用图精度。
- 适合需要稳定感知的自动驾驶与机器人导航场景。
本文扩展了LiDAR-BIND框架，该框架将异构传感器（雷达、声呐）绑定至由激光雷达定义的潜在空间，并引入显式时序一致性机制。提出三项改进：(i) 时序嵌入相似性，对齐连续潜在表示；(ii) 运动对齐变换损失，匹配预测与真实激光雷达间的位移；(iii) 基于专用时序模块的窗口化时序融合。同时优化模型结构以更好保留空间结构。在雷达/声呐到激光雷达的转换任务上，实验显示时序与空间一致性显著提升，使基于Cartographer的SLAM系统轨迹误差更低、占用图精度更高。提出基于Fréchet视频运动距离（FVMD）和相关峰值距离的新度量指标，用于评估时序质量。所提出的时序LiDAR-BIND（LiDAR-BIND-T）在保持模块化融合的同时大幅增强时序稳定性，显著提升下游SLAM的鲁棒性与性能。
原文摘要 · Abstract (English)
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.
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