Pose estimation, tracking, and action recognition of articulated objects from
depth images are important and challenging problems, which are normally
considered separately. In this paper, a unified paradigm based on Lie group
theory is proposed, which enables us to collectively address these related
problems. Our approach is also applicable to a wide range of articulated
objects. Empirically it is evaluated on lab animals including mouse and fish,
as well as on human hand. On these applications, it is shown to deliver
competitive results compared to the state-of-the-arts, and non-trivial
baselines including convolutional neural networks and regression forest
methods.

本文提出了一种基于李群理论的统一框架，用于解决深度图像中复杂目标的姿态估计、跟踪和动作识别问题，并在多种实验中验证其优于卷积神经网络和回归森林等方法的竞争力。

Lie-X: 基于深度图像的关节物体姿态估计、跟踪和动作识别

Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking,  and Action Recognition on Lie Groups

Robots operating in domestic environments generally need to interact with
articulated objects, such as doors, cabinets, dishwashers or fridges. In this
work, we present a novel, probabilistic framework for modeling articulated
objects as kinematic graphs. Vertices in this graph correspond to object parts,
while edges between them model their kinematic relationship. In particular, we
present a set of parametric and non-parametric edge models and how they can
robustly be estimated from noisy pose observations. We furthermore describe how
to estimate the kinematic structure and how to use the learned kinematic models
for pose prediction and for robotic manipulation tasks. We finally present how
the learned models can be generalized to new and previously unseen objects. In
various experiments using real robots with different camera systems as well as
in simulation, we show that our approach is valid, accurate and efficient.
Further, we demonstrate that our approach has a broad set of applications, in
particular for the emerging fields of mobile manipulation and service robotics.

该研究提出了一种用于模拟动态图的概率框架，包括对边模型的参数化和非参数模型的建模，以及如何估算物体运动的结构和运动学模型以及其在机器人操作和移动操作中的应用。