SLAMalgoritmer
SLAMalgoritmer, or SLAM-algoritmer, are methods used to solve the simultaneous localization and mapping problem. They aim to estimate the pose of a moving device (such as a robot or camera) while building a map of an unknown environment, all in real time and without prior knowledge of either the trajectory or the surroundings. The result is a consistent estimate of where the device is within the created map as exploration proceeds.
The field comprises several families of approaches. Filter-based SLAM, including EKF-SLAM and UKF-SLAM, propagates probabilistic estimates
Key challenges in SLAMalgoritmer include data association (matching observations to known map features), loop closure (recognizing
Applications span robotics, autonomous vehicles, unmanned aerial vehicles, and augmented reality. Notable SLAMalgoritmer systems include visual