面向季节性深积雪的“教导与重复”方法
Toward Teach and Repeat Across Seasonal Deep Snow Accumulation
- Northern Robotics Laboratory, Université Laval（北方机器人实验室，拉瓦尔大学）
- University of Toronto Robotics Institute（多伦多大学机器人研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对季节性深积雪环境，本文提出并初步验证了基于激光雷达和雷达的教导与重复方法，实验表明激光雷达在去除地面点后定位更强，雷达在旧地图上可定位但有小偏差，并总结了改进方向。
AI中文摘要：
教导与重复是一种在挑战性地形和越野环境中实现自主性的快速方法。人类操作员驾驶车辆，创建一条条路径网络，这些路径被映射并与里程计数据关联。教导完成后，系统即可在其轨迹内自主行驶。这种精确性使操作员能够确信机器人将沿可通行的路线行驶。然而，这种操作模式在因季节变化而发生显著变化的越野环境中很少被探索。本文介绍了使用激光雷达和雷达实现教导与重复的初步现场试验。利用即将发布的FoMo数据集中的一部分数据，我们尝试重复4天、44天和113天前的路线。激光雷达教导与重复在去除地面点后表现出更强的定位能力。调频连续波雷达通常能在较旧的地图上定位，但仅与教导路径存在小偏差。此外，我们强调了雷达在近期地图上因车辆高俯仰或横滚而导致定位失败的具体案例。我们总结了现场部署中获得的经验教训，并指出了为实现环境季节性变化下可靠的教导与重复而需要改进的方面。请关注数据集网站https://norlab-ulaval.github.io/FoMo-website以获取更新和数据发布信息。
英文摘要：
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.
