LiDAR-camera extrinsic calibration (LCEC) is crucial for data fusion in intelligent vehicles.
Offline, target-based approaches have long been the preferred choice in this field. However …

In multimodal perception systems, achieving precise extrinsic calibration between LiDAR
and camera is of critical importance. Previous calibration methods often required specific …

In this work, we present a novel target-based lidar-camera extrinsic calibration methodology
that can be used for non-overlapping field of view (FOV) sensors. Contrary to previous work …

Current traditional methods for LiDAR-camera extrinsics estimation depend on offline targets
and human efforts, while learning-based approaches resort to iterative refinement for …

The integration of the camera and LiDAR has played an important role in the field of
autonomous driving, for example in visual–LiDAR SLAM and 3D environment fusion …

Sensor fusion is vital for the safe and robust operation of autonomous vehicles. Accurate
extrinsic sensor to sensor calibration is necessary to accurately fuse multiple sensor's data …

Lidar and cameras serve as essential sensors for automated vehicles and intelligent robots,
and they are frequently fused in complicated tasks. Precise extrinsic calibration is the …

Autonomous driving heavily relies on light detection and ranging (LiDAR) and camera
sensors, which can significantly improve the performance of perception and navigation tasks …

Camera-LiDAR extrinsic calibration is a critical task for multi-sensor fusion in autonomous
systems, such as self-driving vehicles and mobile robots. Traditional techniques often …

Determining the extrinsic parameter between multiple light detection and rangings (LiDARs)
and cameras is essential for autonomous robots, especially for solid-state LiDARs, where …