Abstract
Many LiDAR place recognition systems have been developed and tested specifically for urban driving scenarios. Their performance in natural environments such as forests and woodlands have been studied less closely. In this paper, we analyzed the capabilities of four different LiDAR place recognition systems, both handcrafted and learning-based methods, using LiDAR data collected with a handheld device and legged robot within dense forest environments. In particular, we focused on evaluating localization where there is significant translational and orientation difference between corresponding LiDAR scan pairs. This is particularly important for forest survey systems where the sensor or robot does not follow a defined road or path. Extending our analysis we then incorporated the best performing approach, Logg3dNet, into a full 6-DoF pose estimation system -- introducing several verification layers for precise registration. We demonstrated the performance of our methods in three operational modes: online SLAM, offline multi-mission SLAM map merging, and relocalization into a prior map. We evaluated these modes using data captured in forests from three different countries, achieving 80% of correct loop closures candidates with baseline distances up to 5m, and 60% up to 10m.
Abstract (translated)
许多激光雷达点识别系统专门针对城市驾驶场景进行了开发和测试。他们对自然环境（如森林和林地）的表现研究较少。在本文中，我们分析了四种不同LiDAR点识别系统，包括手工制作和学习方法，利用手持设备收集的森林中使用机器人获取的LiDAR数据。特别关注评估在对应LiDAR扫描对之间存在较大平移和方向差异的定位能力。这对于森林调查系统尤为重要，因为传感器或机器人并不遵循明确的路线或路径。通过扩展我们的分析，我们将最佳表现的方法——Logg3dNet，纳入了一个6DoF姿态估计系统——引入了几个验证层以实现精确的注册。我们在三种操作模式下评估了我们的方法：在线SLAM，离线多任务SLAM地图合并和基于先验地图的重新定位。我们使用从三个不同国家收集的森林数据来评估这些模式，达到80%的循环关闭候选者，其基线距离在5米以内，以及60%在10米以内。
URL
https://arxiv.org/abs/2403.14326
