radarLiDAR

RadarLidar refers to a sensor fusion approach that combines radar and lidar technologies to enhance environmental perception for autonomous systems, robotics, and mapping. Radar provides robust long-range detection and velocity information even in adverse weather, while lidar delivers high-resolution three-dimensional point clouds that enable precise obstacle delineation and scene understanding. Together, they aim to compensate for the weaknesses of each modality when used alone.

In a RadarLidar system, the radar and lidar sensors are typically mounted on the same platform and share a synchronized processing pipeline. Data fusion can occur at different levels: measurement-level fusion combines raw radar and lidar returns; feature-level fusion merges extracted features such as object candidates, reflectivity, and velocity; and decision-level fusion fuses sensor-informed decisions or tracks. Calibration and synchronization are essential to align the sensors in space and time, often requiring calibration targets, temporal matching, and common coordinate frames.

Benefits of RadarLidar include improved object detection across a wider range of weather and lighting conditions, better estimation of object velocity through radar Doppler information, and increased redundancy for critical detections. Challenges involve higher cost, greater power and computing requirements, and the need for sophisticated calibration and data association techniques to reconcile differing resolutions, frame rates, and measurement characteristics.

Applications span autonomous driving, mobile robotics, unmanned aerial vehicles, and maritime sensing. Ongoing research addresses optimization of fusion algorithms, real-time processing, and standardized benchmarks to evaluate performance across scenarios.