Map-based localization and sensing are one of the key components in autonomous driving technologies, where high quality 3D map reconstruction is fundamentally utmost important. However, due to the highly dynamic and uncontrollable properties of real world environment, building a high quality 3D map is not straightforward and requires several strong assumptions. To address this challenge, we present a complete framework, which detects and extracts the moving objects from a sequence of unordered and texture-less point clouds, to build high quality static maps. To accurately detect the moving objects from data acquired with a possibly fast moving platform, we propose a novel 3D Flow Field Analysis approach in which we inspect the motion behaviour of the registered point sets. The proposed algorithm elegantly models the temporal and spatial displacement of the moving objects. Thus, both small moving objects ( e.g. walking pedestrians) and large moving objects ( e.g. moving trucks) can be detected effectively. Further, by incorporating the Sparse Subspace Clustering framework, we propose a Sparse Flow Clustering algorithm to group the 3D motion flows under both the constraints of motion similarity and spatial closeness. To this end, the static scene parts and the moving objects can be independently processed to achieve photo-realistic 3D reconstructions. Finally, we show that the proposed 3D Flow Field Analysis algorithm and the Sparse Flow Clustering approach are highly effective for motion detection and segmentation, as exemplified on the KITTI benchmark, and yield high quality reconstructed static-maps as well as rigidly moving objects.

中文翻译：

---

通过3D流场分析检测运动物体

基于地图的定位和传感是自动驾驶技术的关键组成部分之一，高质量的3D地图重建从根本上至关重要。但是，由于现实环境的高度动态性和不可控制的特性，构建高质量的3D地图并不是一件容易的事，并且需要几个强有力的假设。为了解决这一挑战，我们提出了一个完整的框架，该框架可以从一系列无序且无纹理的点云中检测并提取运动对象，以构建高质量的静态地图。为了从使用可能快速移动的平台获取的数据中准确检测出移动物体，我们提出了一种新颖的3D流场分析方法，在该方法中，我们检查了配准点集的运动行为。该算法很好地模拟了运动物体的时空位移。因此，两个小的运动物体（ 例如 行人）和大型运动物体（ 例如移动卡车）可以被有效地检测到。此外，通过合并稀疏子空间聚类框架，我们提出了稀疏流聚类算法，以在运动相似性和空间接近性的约束下对3D运动流进行分组。为此，可以对静态场景部分和运动对象进行独立处理，以实现逼真的3D重建。最后，我们证明了所提出的3D流场分析算法和稀疏流聚类方法对于运动检测和分割非常有效，如KITTI基准所举例说明的那样，并且可以生成高质量的重建静态地图以及刚性移动物体。