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 of pose and map over time. Graph-based SLAM constructs a graph with poses as nodes and sensor-derived constraints as edges, then solves a global optimization problem to minimize error and reduce drift. FastSLAM uses particle filters to manage multiple trajectory hypotheses and factorized maps to improve scalability. Visual SLAM uses cameras to extract features and track them across frames, while lidar-based SLAM relies on laser scans. Many systems fuse multiple sensors to leverage complementary strengths.

Key challenges in SLAMalgoritmer include data association (matching observations to known map features), loop closure (recognizing previously seen areas to correct drift), scale ambiguity in monocular setups, and meeting real-time computational limits. Map representations can be dense, semi-dense, or sparse feature-based, and may be metric (accurate geometry) or map-like structures that support navigation in dynamic environments.

Applications span robotics, autonomous vehicles, unmanned aerial vehicles, and augmented reality. Notable SLAMalgoritmer systems include visual SLAM variants such as ORB-SLAM, lidar-based approaches like LOAM and Cartographer, and multi-sensor solutions such as RTAB-Map, each optimized for different sensors and hardware constraints.