Active Random Hypersurface Models: Simultaneous Shape and Pose Tracking of Extended Objects in Noisy Point Clouds

主动随机超曲面模型：噪声点云中扩展对象的同时形状和姿态跟踪

基本信息

批准号：
234520279
负责人：
Professor Dr.-Ing. Uwe D. Hanebeck
金额：
--
依托单位：
Lehrstuhl für Intelligente Sensor-Aktor-Systeme (ISAS)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2013
资助国家：
德国
起止时间：
2012-12-31 至 2015-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/234520279?language=en
关键词：
Active Random Hypersurface Models Simultaneous

项目摘要

Tracking the pose of quickly moving extended 3D-objects based on noisy point cloud measurements from the surface of the object is an important problem in several applications. These include automobile safety, innovative control for entertainment devices, telepresence applications, and industrial production lines.Measurements are acquired by sensors such as laser scanners, depth cameras, multi-camera setups, or radar devices. In general, due to occlusion effects, only certain parts of the object are visible at a given time step. In addition, depending on where the object is located relative to the sensor, the number of measurements and their quality strongly varies. In order to accurately estimate the pose, several measurements of the object have to be sequentially collected over time while the object is in motion. Tracking the pose also requires the continuous determination of its shape, even if the shape of the target object is not the primary interest, in order to combine information from different perspectives. For shape modeling, the well-established concept of Random Hypersurface Models (RHMs) and Active Contours will be combined, resulting in the ARHMs. This will form the basis of a Bayesian tracking algorithm, which recursively estimates the shape and pose of an object simultaneously. Using RHMs, noisy measurements are related to the object parameters using explicit measurement equations with multiplicative noise. The key idea is to describe the object by applying transformations on a base shape according to a probability distribution. For example, a cylinder can be described as a circular base shape being transformed by an extrusion. This model allows information to be extracted from measurements of a point cloud without knowing where the exact source points were generated. The main research contribution of the proposal is the extension of RHMs to three-dimensional objects. This consists of the development of three new types of RHMs, which can be combined to describe more complex objects, as well as articulated structures. The application of symmetries and transformation invariances avoids redundancies in the representation, allowing even complex forms to be described using a small amount of parameters. The parametrized surface, together with regularization constraints, allows modeling of parts that have not been observed. This concept is based on ideas from Active Contours. The non-trivial combination of RHMs and Active Contours will yield an estimation method capable of reliably tracking the pose of extended 3D-objects using a parametrized form, resulting in an efficient model that allows the derivation of estimation procedures in closed form. In addition, we expect our proposed ideas and algorithms to result in a significant contribution to the field of 3D-object tracking, as our approach is based on solid mathematical fundaments, yet will still be intuitive.

在几种应用中，基于对象表面的嘈杂点云测量值跟踪快速移动扩展3D对象的姿势是一个重要的问题。其中包括汽车安全，娱乐设备的创新控制，远程呈现应用和工业生产线。计量由激光扫描仪，深度摄像头，多相机设置或雷达设备等传感器获得。通常，由于遮挡效应，只有对象的某些部分在给定的时间步骤中可见。另外，取决于对象相对于传感器的位置，测量数及其质量的数量有很强的变化。为了准确估计姿势，在物体运动中时，必须随时间依次收集对象的几个测量值。跟踪姿势还需要连续确定其形状，即使目标对象的形状不是主要兴趣，以便从不同角度组合信息。对于形状建模，将组合出良好的随机超表面模型（RHM）和主动轮廓的概念，从而导致ARHM。这将构成贝叶斯跟踪算法的基础，该算法同时递归估计对象的形状和姿势。使用RHMS，使用具有乘法噪声的显式测量方程与对象参数有关。关键思想是通过根据概率分布在基本形状上应用转换来描述对象。例如，圆柱体可以描述为通过挤压转化的圆形底部形状。该模型允许从点云的测量值中提取信息，而无需知道确切的源点的生成位置。该提案的主要研究贡献是将RHMS扩展到三维对象。这包括三种新型RHM的开发，可以将它们组合起来以描述更复杂的对象以及铰接的结构。对称和转化不变的应用避免了表示形式的冗余，从而可以使用少量参数来描述复杂的形式。参数化表面以及正则化约束允许对未观察到的零件进行建模。这个概念基于主动轮廓的思想。 RHMS和Active Contours的非平地组合将产生一种估计方法，能够使用参数化形式可靠地跟踪扩展3D对象的姿势，从而产生有效的模型，该模型允许以封闭形式衍生估计过程。此外，我们期望我们提出的思想和算法会为3D对象跟踪的领域做出重大贡献，因为我们的方法基于坚实的数学基本基础，但仍然是直观的。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Shape tracking using Partial Information Models

使用部分信息模型进行形状跟踪

DOI：
10.1109/mfi.2015.7295736
发表时间：
2015
期刊：
2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
影响因子：
0
作者：
Antonio;Florian Faion;Uwe D. Hanebeck
通讯作者：
Uwe D. Hanebeck

Tracking extended objects using extrusion Random Hypersurface Models