Targetless Calibration

Targetless calibration aims to automatically determine the relative positions and orientations of multiple sensors (e.g., cameras, lidar, radar) without relying on artificial targets, streamlining sensor integration for applications like autonomous driving. Recent research focuses on developing robust algorithms that leverage implicit neural representations, point cloud occlusion relationships, or common features extracted from diverse sensor data, often incorporating optimization techniques like RANSAC and Levenberg-Marquardt for improved accuracy. These advancements enable more efficient and practical sensor fusion, improving the reliability and performance of perception systems in dynamic environments. The resulting accurate sensor calibration is crucial for enhancing the safety and capabilities of autonomous vehicles and robotics systems.