SLAMbased

SLAMbased refers to methods, systems, or algorithms that rely on simultaneous localization and mapping (SLAM) to estimate a device's pose while building a map of the surrounding environment. These systems aim to reduce odometry drift by incorporating observations from sensors such as cameras, LiDAR, or radar, enabling robust operation in real time or near real time. SLAM-based approaches are widely used in mobile robotics, autonomous vehicles, and augmented reality.

SLAM techniques can be broadly classified as filter-based and optimization-based. Filter-based SLAM, such as EKF-SLAM, maintains a probabilistic estimate of pose and features using recursive updates. Optimization-based SLAM, including pose-graph SLAM and bundle adjustment, formulates a global optimization problem over a window or the entire trajectory to minimize reprojection or matching errors. A common component in both is data association and loop closure, which detect revisited areas to correct drift. Real-time variants balance accuracy with computational efficiency.

Sensor modalities drive SLAM performance. Visual SLAM uses cameras (monocular, stereo, or RGB-D) and may fuse inertial measurements in VIO for robustness. LiDAR-based SLAM uses 3D point clouds for precise geometry, often with loop closure at large scales. Some SLAM systems combine modalities, and map representations range from dense occupancy grids to sparse feature-based maps or dense point clouds.

Applications include autonomous navigation for ground and aerial vehicles, service robots, and mixed reality devices. Common challenges encompass drift over long trajectories, dynamic environments where moving objects cause false correspondences, loop-closure detection in scale-rich scenes, and computational constraints for real-time operation. Evaluation typically reports trajectory error, map accuracy, and robustness under sensor degradation.