Mapping in unstructured natural environment: a sensor fusion framework for wearable sensor suites
Mapping in unstructured natural environment: a sensor fusion framework for wearable sensor suites
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DOI：
10.1007/s42452-021-04555-y
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发表时间：
2021-05-01
影响因子：
2.6
通讯作者：
Pradalier, Cedric
中科院分区：
文献类型：
--
作者：
Chahine, Georges;Vaidis, Maxime;Pradalier, Cedric
我们提出了一个广义的映射框架，可以承受的挑战，在非结构化的户外环境中工作，如积雪的森林。所提出的方法利用传感器融合方案，其中使用相机和激光雷达等传感器来重建周围的自然环境。虽然映射技术，如SLAM和ICP本身不能正确地处理自然场景的复杂性，他们确实有潜力在一个拟议的传感器融合方案，基于因子图架构的全球解决方案作出贡献。在本文中，我们提出了一个创新的地图配准方案的视觉地图，并显示它如何可以提高重建质量后的数据融合。我们还分析了因素图的行为和敏感性的不确定性，通过比较残差与不同的参数组合，如方差，使用穷举网格搜索与地面真理比较。最后，我们提出了一个ICP推断环路闭合，能够补偿位置和姿态漂移。实验是通过使用可穿戴传感器套件在多雪的森林中进行记录来进行的。在实验中，使用毫米精度的全站仪获得地面实况。所提出的框架被证明是强大的，同样能够提供估计，否则无法实现使用经典的技术，如视觉SLAM和ICP激光。最后，在地图重建质量的一个明显的改善，所提出的框架实现了0.36米的翻译误差。
We present a generalized mapping framework that can withstand the challenges incurred by working in unstructured outdoor environments, such as a snowy forest. The proposed method takes advantage of a sensor fusion scheme, where sensors such as cameras and lidars are used in order to reconstruct the surrounding natural environment. Although mapping techniques such as SLAM and ICP cannot themselves properly handle the complexity of natural scenes, they do have the potential to contribute to the global solution in a proposed sensor fusion scheme, based on a factor graph architecture. In this paper, we propose an innovative map registration scheme for visual maps, and show how it can improve the reconstruction quality after data fusion. We also analyze the behavior and sensitivity of factor graphs to uncertainties, by comparing the residual error with different parameter combinations such as variances, using an exhaustive grid search with ground truth comparison. Finally, we suggest an ICP-inferred loop closure, capable of compensating position and attitude drift. The experiments are carried out by recording in a snowy forest using a wearable sensor suite. In the experiments, ground truth was acquired using a millimeter-accurate total station. The proposed framework is shown to be robust and likewise capable of providing estimates that are otherwise unattainable using classic techniques, such as visual SLAM and ICP for lasers. Finally, a visible improvement in the map reconstruction quality is shown, and the proposed framework achieves a translation error of 0.36 m.