This paper introduces a novel targetless method for joint intrinsic and extrinsic calibration of
LiDAR-camera systems using plane-constrained bundle adjustment (BA). Our method …

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

Multimodal perception represents a vital approach for observing non-cooperative targets in
space. The accurate determination of the extrinsic parameters for monocular camera and …

Robust and reliable calibration forms the foundation of efficient multi-sensor fusion. Most
existing calibration methods are offline and rely on artificial targets, which is time consuming …

Recently, with the rapid progress of Solid-State LiDAR (SSL), the issues of establishing a
robust transformation between SSL and other sensors have attracted considerable attention …

Applying LiDAR and camera fusion sensing in roadside environments enhances traffic
information accuracy compared to single-sensor systems. Achieving precise spatial …

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 …

Autonomous vehicles are equipped with a multi-modal sensor setup to enable the car to
drive safely. The initial calibration of such perception sensors is a highly matured topic and …

Precise and robust extrinsic parameter calibration is fundamental for LiDAR-camera multi-
modal sensing applications. However, most existing methods assume that sensors have the …

The extrinsic transformation between LiDAR and camera is 6-DoF, but the motion of robot is
mainly 3-DoF. The classical hand–eye calibration method used in unmanned aerial vehicles …