Object tracking is a key aspect in many applications such as augmented
reality in medicine (e.g. tracking a surgical instrument) or robotics. Squared
planar markers have become popular tools for tracking since their pose can be
estimated from their four corners. While using a single marker and a single
camera limits the working area considerably, using multiple markers attached to
an object requires estimating their relative position, which is not trivial,
for high accuracy tracking. Likewise, using multiple cameras requires
estimating their extrinsic parameters, also a tedious process that must be
repeated whenever a camera is moved.
This work proposes a novel method to simultaneously solve the above-mentioned
problems. From a video sequence showing a rigid set of planar markers recorded
from multiple cameras, the proposed method is able to automatically obtain the
three-dimensional configuration of the markers, the extrinsic parameters of the
cameras, and the relative pose between the markers and the cameras at each
frame. Our experiments show that our approach can obtain highly accurate
results for estimating these parameters using low resolution cameras.
Once the parameters are obtained, tracking of the object can be done in real
time with a low computational cost. The proposed method is a step forward in
the development of cost-effective solutions for object tracking.

本文提出了一种新的方法，同时解决了使用固定平面标记来进行多相机三维物体跟踪所面临的问题，能够在低分辨率摄像头下获得高精度的跟踪结果，从而在实现物体实时跟踪的同时，大大降低了计算成本。

使用平面正方形标记同时进行多视角相机姿态估计和物体跟踪

Simultaneous Multi-View Camera Pose Estimation and Object Tracking with  Square Planar Markers

Planar markers are useful in robotics and computer vision for mapping and
localisation. Given a detected marker in an image, a frequent task is to
estimate the 6DOF pose of the marker relative to the camera, which is an
instance of planar pose estimation (PPE). Although there are mature techniques,
PPE suffers from a fundamental ambiguity problem, in that there can be more
than one plausible pose solutions for a PPE instance. Especially when
localisation of the marker corners is noisy, it is often difficult to
disambiguate the pose solutions based on reprojection error alone. Previous
methods choose between the possible solutions using a heuristic criteria, or
simply ignore ambiguous markers.
We propose to resolve the ambiguities by examining the consistencies of a set
of markers across multiple views. Our specific contributions include a novel
rotation averaging formulation that incorporates long-range dependencies
between possible marker orientation solutions that arise from PPE ambiguities.
We analyse the combinatorial complexity of the problem, and develop a novel
lifted algorithm to effectively resolve marker pose ambiguities, without
discarding any marker observations. Results on real and synthetic data show
that our method is able to handle highly ambiguous inputs, and provides more
accurate and/or complete marker-based mapping and localisation.

本篇论文提出了一种通过检测无人机平面标记的多个视角的一致性来解决定位问题中的歧义问题的方法。该方法可以更有效地解决标记姿态歧义问题，并提供更精确、完整的基于标记的映射和定位。