在季节性积雪环境下测试教-重复导航，验证雷达与激光雷达的长期路径重演能力。
Toward Teach and Repeat Across Seasonal Deep Snow Accumulation
- 使用激光雷达和调频连续波雷达实现教-重复导航，通过里程计建图
- 4天至113天旧地图下，雷达可保持小偏差定位，激光雷达去地表点后定位更强
- 车辆俯仰/滚转大时雷达定位失败，适合研究野外环境长期自主导航的团队
教-重复是一种在复杂地形和非铺装路面快速实现自主的方法。人类操作员驾驶车辆生成路径网络，系统通过里程计进行地图构建并关联轨迹，教学完成后即可沿原路径自主行驶，确保操作者对机器人路径安全性的信心。然而，该方法在受季节变化显著影响的非铺装环境中尚未深入探索。本文基于即将发布的FoMo数据集的子集，开展了初步实地试验，尝试重复4天、44天和113天前的教学路线。结果表明，激光雷达在去除地面点后具有更强的定位能力；调频连续波（FMCW）雷达在旧地图上通常能实现小偏差定位，但高俯仰或滚转状态下，即使在新地图上也会出现定位失败。文中总结了现场部署中的关键经验，并指出了提升季节性环境下发教-重复可靠性的改进方向。数据集更新信息请关注 https://norlab-ulaval.github.io/FoMo-website。
原文摘要 · Abstract (English)
Teach and repeat is a rapid way to achieve autonomy in challenging terrain and off-road environments. A human operator pilots the vehicles to create a network of paths that are mapped and associated with odometry. Immediately after teaching, the system can drive autonomously within its tracks. This precision lets operators remain confident that the robot will follow a traversable route. However, this operational paradigm has rarely been explored in off-road environments that change significantly through seasonal variation. This paper presents preliminary field trials using lidar and radar implementations of teach and repeat. Using a subset of the data from the upcoming FoMo dataset, we attempted to repeat routes that were 4 days, 44 days, and 113 days old. Lidar teach and repeat demonstrated a stronger ability to localize when the ground points were removed. FMCW radar was often able to localize on older maps, but only with small deviations from the taught path. Additionally, we highlight specific cases where radar localization failed with recent maps due to the high pitch or roll of the vehicle. We highlight lessons learned during the field deployment and highlight areas to improve to achieve reliable teach and repeat with seasonal changes in the environment. Please follow the dataset at https://norlab-ulaval.github.io/FoMo-website for updates and information on the data release.
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