With the ongoing spread of autonomous vehicles, challenges like obstacle avoidance get more important. To realize obstacle avoidance, a reliable obstacle detection is one of the preconditions. While common autonomous vehicles mainly use camera and radar sensors for this purpose, currently laser range sensors are enforcing as alternatives. Due to its high accuracy, this kind of sensor establishes in different industries. In general, the sensor data is used as point clouds. Within this master's thesis, an approach for obstacle detection based on these point clouds is presented. Therefore, several subtasks, e.g. downsampling and plane segmentation, of a reliable obstacle detection are carried out. Finally, an algorithm for obstacle tracking, based on a linear Kalman filter, is implemented. The received results are evaluated within several test drives of the autonomous vehicle MadeInGermany (MIG).