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 uninterpratable 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.
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,这是生命导航任务的一个重要特征。D2D方法依赖于静态场景的假设,因此很容易在频谱阴影、自我隔离、移动对象和扭曲文物等记录设备之间移动时产生偏差。深学习方法可以通过放松这些限制,在动态场景中实现更高的准确性。然而,DNND可以产生无法互换的解决方案,而从安全角度来说,这些解决方案可能存在问题。在本文件中,我们建议了下取样LIDAR点云体云的方法,以排除违反静态场假设的对立体云体,并将错误引入D扫描匹配进程。我们的方法使用一个解决方案一致性过滤器,在D2D的贡献与基于点网络的当地估计不一致的地方数据时,识别和标记氧化物。