Long-term autonomy requires robust navigation in environments subject to dynamic and static changes, as well as adverse weather conditions. Teach-and-Repeat (T\&R) navigation offers a reliable and cost-effective solution by avoiding the need for consistent global mapping; however, existing T\&R systems lack a systematic solution to tackle various environmental variations such as weather degradation, ephemeral dynamics, and structural changes. This work proposes LTR$^2$, the first cross-modal, cross-platform LiDAR-Teach-and-Radar-Repeat system that systematically addresses these challenges. LTR$^2$ leverages LiDAR during the teaching phase to capture precise structural information under normal conditions and utilizes 4D millimeter-wave radar during the repeating phase for robust operation under environmental degradations. To align sparse and noisy forward-looking 4D radar with dense and accurate omnidirectional 3D LiDAR data, we introduce a Cross-Modal Registration (CMR) network that jointly exploits Doppler-based motion priors and the physical laws governing LiDAR intensity and radar power density. Furthermore, we propose an adaptive fine-tuning strategy that incrementally updates the CMR network based on localization errors, enabling long-term adaptability to static environmental changes without ground-truth labels. We demonstrate that the proposed CMR network achieves state-of-the-art cross-modal registration performance on the open-access dataset. Then we validate LTR$^2$ across three robot platforms over a large-scale, long-term deployment (40+ km over 6 months), including challenging conditions such as nighttime smoke. Experimental results and ablation studies demonstrate centimeter-level accuracy and strong robustness against diverse environmental disturbances, significantly outperforming existing approaches.

翻译：摘要：长期自主运行要求机器人在动态与静态环境变化以及恶劣天气条件下具备鲁棒的导航能力。教与复现（T&R）导航通过避免对一致全局地图的需求，提供了一种可靠且成本有效的解决方案；然而，现有T&R系统缺乏系统性方案应对天气退化、瞬时动态和结构变化等多种环境变异。本文提出LTR$^2$，这是首个跨模态、跨平台的LiDAR-教与-雷达-复现系统，系统性地解决了上述挑战。LTR$^2$在教学阶段利用LiDAR在正常条件下捕获精确的结构信息，并在复现阶段采用4D毫米波雷达以实现在环境退化下的鲁棒运行。为将稀疏且有噪声的前视4D雷达数据与稠密且精准的全向3D LiDAR数据对齐，我们引入了一种跨模态配准（CMR）网络，该网络联合利用基于多普勒的运动先验以及支配LiDAR强度与雷达功率密度的物理规律。此外，我们提出一种自适应微调策略，基于定位误差逐步更新CMR网络，从而在无真值标签的情况下实现对静态环境变化的长期适应性。我们证明了所提CMR网络在公开数据集上达到了最先进的跨模态配准性能。随后，我们在三个机器人平台上对LTR$^2$进行了大规模、长期部署验证（6个月内运行超40公里），包括夜间烟雾等挑战性条件。实验结果与消融研究表明，该系统实现了厘米级精度，并对多种环境干扰展现出强鲁棒性，显著优于现有方法。