Distribution-to-distribution (D2D) point cloud registration techniques such as the Normal Distributions Transform (NDT) can align point clouds sampled from unstructured scenes and provide accurate bounds of their own solution error covariance -- an important feature for safety-of-life navigation tasks. D2D methods rely on the assumption of a static scene and are therefore susceptible to bias from range-shadowing, self-occlusion, moving objects, and distortion artifacts as the recording device moves between frames. Deep Learning-based approaches can achieve higher accuracy in dynamic scenes by relaxing these constraints, however, DNNs produce uninterpretable solutions which can be problematic from a safety perspective. In this paper, we propose a method of down-sampling LIDAR point clouds to exclude voxels that violate the assumption of a static scene and introduce error to the D2D scan matching process. Our approach uses a solution consistency filter -- identifying and flagging voxels where D2D contributions disagree with local estimates from a PointNet-based registration network. Our results show that this technique provides significant benefits in registration accuracy, and is particularly useful in scenes containing dense foliage.

翻译：分布到分布(D2D)点云登记技术,如正常分布变换(NDT)可以将从未结构化的场景中取样的点云云与从未结构化的场景中取样的云体相匹配,并提供其自身解决方案错误共差的准确界限 -- -- 这是使用寿命安全导航任务的一个重要特征。 D2D方法依赖于静态场景的假设,因此很容易产生分布到分布到分布(D2D)点云层的偏差,并因此在记录设备在框架之间移动时会出现偏差。深学习方法可以通过放松这些限制而在动态场景中实现更高的准确性,然而,DNNP会产生无法解释的解决方案,从安全角度来说可能会产生问题。在本文中,我们提出了下取样LIDAR点云体云的方法,以排除违反静态场景假设的氧化物,并将错误引入D2D扫描匹配过程。我们的方法使用一个解决方案一致性过滤器 -- 确定和标记氧化物,因为D2D的贡献与基于点网络的当地估计不一致。我们的结果显示,这种技术在登记准确性方面有很大的好处,并且在含有密片片的场中特别有用。