Multi Robot Localization
Localization is one of the most fundamental tasks for mobile robots. For a team of multiple robots, a paradigm called Collaborative Localization (CL) has been demonstrated to provide significant improvements of the localization performance of the individual teammates. In CL, robots detect each other and communicate their estimates, which correlates the estimates of their individual poses. It is of fundamental importance to keep track of these dependencies to avoid the problem of double-counting or data-incest, which occurs when two robots treat shared information as uncorrelated.
In many relevant multi-robot applications -- for example underwater, in mines, or in large-scale environments, -- communication might be energy consuming, error-prone, slow, or simply not possible at all times. Therefore, decentralized architectures that reduce the need for communication to a minimum are desirable.
We restrict the communications to places where the robots actually meet. Thus, exchange of information takes place only between two
robots obtaining a relative measurement. We developed an EKF-based localization algorithm that is able to approximate the covariance
under the above-mentioned constraint.
Motion capture tracking of humans and animals
The aim in this project is the development of an online motion capture tracking system, which is able to track complicated human and animal motions in a robust way. More precisely we glue indistinguishable markers on arbitrary positions of the skin of the mammal and measure their positions in motion. The main task are to initialize the method, to associate the resulting data positions to the right markers and find the underlying skeleton structure.While many existing approaches can deal with the latter two problems, they typically need a specific pose for initialization. As this is rather unpractical in the context of animal tracking this often requires a manual initialization process. We developed an approach to reliably track animals and humans in marker-based optical motion capture systems with freely attached markers that is also able to perform an automatic initialization without any pre- or post-processing of the data.
Many existing skeleton tracking approaches use a predefined skeleton structure, which is globally scaled to the persons or animals height. Detailed skeleton parameter studies only exists for some kind of animals and additionally there is a lot of variation in the skeleton bone parameter relations between different mammal breeds. We developed an approach for an automatic bone parameter estimation during tracking.
Detection of fine motor disabilities of stroke patients
Direct after a stroke many patients have motor disabilities. These disabilities constrain their normal live for a long time after the stroke. So a fast regain of the former abilities is a desirable issue. In order to evaluate medical treatments an objective measure is needed. In this project we use a group of stroke patients, which have an unilateral disability of one hand. These stroke patients have to execute motor tests, like the nine hole peg test, while wearing data gloves from CyberGlove. Then we compare the differences in movement strategies, trajectories and acceleration of the healthy hand to the unhealthy hand. Thereby we use a Gaussian process dynamical model.