Abstract
The ability to map challenging sub-arctic environments opens new horizons for robotic deployments in industries such as forestry, surveillance, and open-pit mining. In this paper, we explore possibilities of large-scale lidar mapping in a boreal forest. Computational and sensory requirements with regards to contemporary hardware are considered as well. The lidar mapping is often based on the SLAM technique relying on pose graph optimization, which fuses the Iterative Closest Point (ICP) algorithm, Global Navigation Satellite System (GNSS) positioning, and Inertial Measurement Unit (IMU) measurements. To handle those sensors directly within the ICP minimization process, we propose an alternative approach of embedding external constraints. Furthermore, a novel formulation of a cost function is presented and cast into the problem of handling uncertainties from GNSS and lidar points. To test our approach, we acquired a large-scale dataset in the Foret Montmorency research forest. We report on the technical problems faced during our winter deployments aiming at building 3D maps using our new cost function. Those maps demonstrate both global and local consistency over 4.1km.
Abstract (translated)
绘制具有挑战性的亚北极环境地图的能力为机器人在林业、监测和露天采矿等行业的部署开辟了新的视野。本文探讨了在北方森林中进行大规模激光雷达测绘的可能性。同时也考虑了现代硬件的计算和感官要求。激光雷达测绘通常基于基于基于姿态图优化的SLAM技术，它融合了迭代最近点（ICP）算法、全球导航卫星系统（GNSS）定位和惯性测量单元（IMU）测量。为了在ICP最小化过程中直接处理这些传感器，我们提出了一种嵌入外部约束的替代方法。此外，本文还提出了一种新的成本函数公式，并将其应用于处理来自GNSS和激光雷达点的不确定性问题。为了测试我们的方法，我们在前一个月的研究森林中获得了一个大规模的数据集。我们报告了在冬季部署期间面临的技术问题，旨在使用我们的新成本函数构建3D地图。这些地图显示了超过4.1公里的全球和本地一致性。
URL
https://arxiv.org/abs/1904.07814
