Realtime shape estimation of continuum objects and manipulators is essential
for developing accurate planning and control paradigms. The existing methods
that create dense point clouds from camera images, and/or use distinguishable
markers on a deformable body have limitations in realtime tracking of large
continuum objects/manipulators. The physical occlusion of markers can often
compromise accurate shape estimation. We propose a robust method to estimate
the shape of linear deformable objects in realtime using scattered and
unordered key points. By utilizing a robust probability-based labeling
algorithm, our approach identifies the true order of the detected key points
and then reconstructs the shape using piecewise spline interpolation. The
approach only relies on knowing the number of the key points and the interval
between two neighboring points. We demonstrate the robustness of the method
when key points are partially occluded. The proposed method is also integrated
into a simulation in Unity for tracking the shape of a cable with a length of
1m and a radius of 5mm. The simulation results show that our proposed approach
achieves an average length error of 1.07% over the continuum's centerline and
an average cross-section error of 2.11mm. The real-world experiments of
tracking and estimating a heavy-load cable prove that the proposed approach is
robust under occlusion and complex entanglement scenarios.

提出了一种使用散乱和无序关键点实时估计线性可变形物体形状的强鲁棒方法，并通过模拟实验和真实世界实验验证了其在追踪和估计复杂情况下的稳健性。

可变形线性物体的实时鲁棒形状估计

Realtime Robust Shape Estimation of Deformable Linear Object

Automated tracking of animal movement allows analyses that would not
otherwise be possible by providing great quantities of data. The additional
capability of tracking in realtime - with minimal latency - opens up the
experimental possibility of manipulating sensory feedback, thus allowing
detailed explorations of the neural basis for control of behavior. Here we
describe a new system capable of tracking the position and body orientation of
animals such as flies and birds. The system operates with less than 40 msec
latency and can track multiple animals simultaneously. To achieve these
results, a multi target tracking algorithm was developed based on the Extended
Kalman Filter and the Nearest Neighbor Standard Filter data association
algorithm. In one implementation, an eleven camera system is capable of
tracking three flies simultaneously at 60 frames per second using a gigabit
network of nine standard Intel Pentium 4 and Core 2 Duo computers. This
manuscript presents the rationale and details of the algorithms employed and
shows three implementations of the system. An experiment was performed using
the tracking system to measure the effect of visual contrast on the flight
speed of Drosophila melanogaster. At low contrasts, speed is more variable and
faster on average than at high contrasts. Thus, the system is already a useful
tool to study the neurobiology and behavior of freely flying animals. If
combined with other techniques, such as `virtual reality'-type computer
graphics or genetic manipulation, the tracking system would offer a powerful
new way to investigate the biology of flying animals.

描述了一个跟踪动物运动的自动化系统，并使用实时跟踪和多目标跟踪算法将动物的运动和行为控制与视觉反馈联系起来，以观察其神经基础，同时利用该系统测量果蝇飞行速度与视觉对比度的关系。