SimTrack: A simulation-based framework for scalable real-time object pose detection and tracking
SimTrack: A simulation-based framework for scalable real-time object pose detection and tracking
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DOI：
10.1109/iros.2015.7353536
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
Karl Pauwels;D. Kragic
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文献类型：
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作者：
Karl Pauwels;D. Kragic
我们提出了一种新的实时目标姿态检测和跟踪方法，该方法根据跟踪的目标数量和观察场景的摄像机数量具有高度的可扩展性。这种可伸缩性的关键是所用算法的高度并行性。该方法维护由多个对象以及在其上操作的机器人组成的场景的单个3D模拟模型。这允许从每个摄影机视点快速合成外观、深度和遮挡信息。该信息既用于更新姿势估计，也用于提取低级视觉线索。使用约束优化方法将从每个摄像机获得的视觉线索有效地融合回单个一致的场景表示。集中的场景表示，加上它实现的可靠性措施，简化了跨多个摄像头的姿势跟踪和姿势检测之间的交互。我们在一个真实的操作场景中展示了我们方法的健壮性。我们公开将这项工作作为用于实时姿势估计的通用ROS软件框架SimTrack的一部分发布，该框架可以很容易地集成到不同的机器人应用程序中。
We propose a novel approach for real-time object pose detection and tracking that is highly scalable in terms of the number of objects tracked and the number of cameras observing the scene. Key to this scalability is a high degree of parallelism in the algorithms employed. The method maintains a single 3D simulated model of the scene consisting of multiple objects together with a robot operating on them. This allows for rapid synthesis of appearance, depth, and occlusion information from each camera viewpoint. This information is used both for updating the pose estimates and for extracting the low-level visual cues. The visual cues obtained from each camera are efficiently fused back into the single consistent scene representation using a constrained optimization method. The centralized scene representation, together with the reliability measures it enables, simplify the interaction between pose tracking and pose detection across multiple cameras. We demonstrate the robustness of our approach in a realistic manipulation scenario. We publicly release this work as a part of a general ROS software framework for real-time pose estimation, SimTrack, that can be integrated easily for different robotic applications.