Simultaneous Localization And Mapping, or SLAM for short, is a very prominent field of research in robotics and other fields where autonomous agents must be able to navigate their sorroundings. In one sentence, the problem is that of a robot placed in an environment who is only equipped with sensoric input and has to gradually build a map of its environment (Mapping) while simultaneously estimate its own position within that map (Localization). While Mapping and Localization are extensive research topics themselves, ranging from the employment of data structures such as graphs and trees to problems in image recognition, the simultaneous application of both fields makes it especially interesting from a numerical point of view. On one hand, the overall problem can be formulated as a nonlinear optimization problem requiring solution methods such as Gradient Descent or Relaxation methods. On the other hand, since the sensoric data is always afflicted with uncertainty, a probabilistic approach is necessary requiring the application of e.g. Bayesian Filtering.
If you are interested, please register via e-mail to ed tod nnob-inu tod sni ta portnera tod b@foo tod de. The schedule is not yet fixed and will be created once the number of participants is known. There will be an initial online meeting to discuss the potential topics and research papers (beginning of April). Details to this meeting (Zoom link, time etc.) will be sent to those who sent an e-mail confirming their interest in the seminar.
After that, the participants will be asked to suggest their top two or three choices, based on which we try to optimize and assign the topics to everyone.
Thursdays at 14:15
FHA7, Seminar room 2.035. Depending on the state of the pandemic, we might resort to Zoom on short notice.