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 …

With the rapid development of autonomous driving, robotics, and intelligent transportation,
multi-sensor-based environment sensing technology for intelligent vehicles has become a …

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

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

Existing target segmentation methods are typically considered easy to implement and yield
satisfactory results. However, they generally cannot adapt to a new environment without …

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 …

Deep learning is widely used in autonomous vehicles' environment perception systems that
utilize data from a variety of sensors such as camera, LiDAR, RADAR, and Time-of-Flight. In …