Best Fit Line

Best-fit line estimation, a fundamental problem across numerous scientific fields, aims to find the line that optimally represents a dataset, often in the presence of noise or outliers. Current research focuses on robust methods for handling noisy data and incorporating diverse feature types, such as points and lines, using techniques like graph neural networks and invariant extended Kalman filters. These advancements are crucial for applications ranging from computer vision (pose estimation, object tracking) and robotics (navigation, mapping) to medical imaging and machine learning (adversarial robustness, OOD detection), improving accuracy and efficiency in various real-world scenarios.