Mechanical Engineering Department, Soongsil University, Seoul 06978, Republic of Korea.
Sensors (Basel). 2023 Feb 28;23(5):2671. doi: 10.3390/s23052671.
This paper proposed a multi-layered 3D NDT (normal distribution transform) scan-matching approach for robust localization even in the highly dynamic environment of warehouse logistics. Our approach partitioned a given 3D point-cloud map and the scan measurements into several layers regarding the degree of environmental changes in the height direction and computed the covariance estimates for each layer using 3D NDT scan-matching. Because the covariance determinant is the estimate's uncertainty, we can determine which layers are better to use in the localization in the warehouse. If the layer gets close to the warehouse's floor, the degree of environmental changes, such as the cluttered warehouse layout and position of boxes, would be significantly large, while it has many good features for scan-matching. If the observation at a specific layer is not explained well enough, then the layer for localization can be switched to other layers with lower uncertainties. Thus, the main novelty of this approach is that localization robustness can be improved even in very cluttered and dynamic environments. This study also provides the simulation-based validation using Nvidia's Omniverse Isaac sim and detailed mathematical descriptions for the proposed method. Moreover, the evaluated results of this study can be a good starting point for further mitigating the effects of occlusion in warehouse navigation of mobile robots.
本文提出了一种多层 3D NDT（正态分布变换）扫描匹配方法，即使在仓库物流这样高度动态的环境中，也能实现稳健的定位。我们的方法根据高度方向上环境变化的程度，将给定的 3D 点云地图和扫描测量值划分为几个层，并使用 3D NDT 扫描匹配为每个层计算协方差估计。由于协方差行列式是估计的不确定性，因此我们可以确定在仓库中定位时哪些层更好用。如果层接近仓库的地面，那么环境变化的程度，例如仓库布局的混乱和箱子的位置，就会非常大，而这对于扫描匹配有很多好处。如果特定层的观测结果解释得不够好，那么可以切换到其他不确定性较低的层进行定位。因此，该方法的主要新颖之处在于，即使在非常混乱和动态的环境中，也可以提高定位的稳健性。本研究还使用 Nvidia 的 Omniverse Isaac sim 进行了基于模拟的验证，并对所提出的方法进行了详细的数学描述。此外，本研究的评估结果可以为进一步减轻移动机器人在仓库导航中的遮挡影响提供一个良好的起点。
