Multi-sensor collaborative perception has been a significant trend in self-driving and robot
navigation. The precondition for multi-sensor fusion is the accurate calibration between …

Multi-sensor fusion is for enhancing environment perception and 3D reconstruction in self-
driving and robot navigation. Calibration between sensors is the precondition of effective …

The integration of cameras and LiDAR sensors has emerged as a promising approach to
enhance environmental perception and 3-D reconstruction capabilities in autonomous …

3D LiDAR and 2D camera are widely used in environment perception tasks in autonomous
driving and robot navigation. The prerequisite for multi-sensor data fusion is to calibrate …

The accurate and robust calibration result of sensors is considered as an important building
block to the follow-up research in the autonomous driving and robotics domain. The current …

The working environment of autonomous driving and robot navigation is so complex and
dynamic that a single type of sensor is insufficient for performing object detection. Thus, in …

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 …

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

Sensor setups of robotic platforms commonly include both camera and LiDAR as they
provide complementary information. However, fusing these two modalities typically requires …

LiDAR-Camera online calibration is of great significance for building a stable autonomous
driving perception system. For online calibration, a key challenge lies in constructing a …