Die nachfolgende Aufgabenstellung ist in englischer Sprache verfasst.
Die Bearbeitung der Themen kann, je nach Wunsch des Studenten,
wahlweise in Englisch oder Deutsch erfolgen.
Localization of Mobile Robot based on Laser Data
One of the basic requirements for navigation of a mobile robot is to be able to estimate its own state (position and orientation in case of mobile robots)
accurately. Most localization algorithms are based on the assumption that a map of the environment already exists, however sometimes a map of the environment
might not be available (such is the case when robots navigate in outdoor environments). Additionaly the presence of dynamic objects in the environment (which are not present in the map) in someway affect the localization algorithm as well. Localization can be done by using either an estimator (such as kalman or Particle filer) or scan matching of raw laser data. Scan matching utilizes a cost function to match the current laser scan with the previous scan and
does not require an explicit map of the environment.
Approach:
1) Implement scan matching algorithm for estimation of position and orientation of a mobile robot. The initial direction would be to filter out dynamic elements before matching of laser scans. Further enhancement can be done by explicitly developing a dynamic map of objects too.
2) Utilizing estimators (Kalman Filter or Particle filter) for state estimation given a motion model and predictive sensor model
3) Comparison of above mentioned techniques in terms of some substantional metric
Prerequisites:
- Programming skills C/C++
Helpful but not required:
- Acquainted with Linux
- Experience with ROS
- Your own Laptop with a running Ubuntu version
Supervisor:
- Sheraz Khan (Contact: sheraz@lsr.ei.tum.de)
References:
- Probablistic Robotics by Sebastian Thrun, Wolfram Burgard and Dieter Fox
- Introduction to Autonomous Mobile Robots by Roland Siegwart and Illah R. Nourbakhsh
- Fundamentals of Statistical Signal Processing by Steven M. Kay