Driven by deep learning techniques, perception technology in autonomous driving has
developed rapidly in recent years, enabling vehicles to accurately detect and interpret …

With the rapid advancements of sensor technology and deep learning, autonomous driving
systems are providing safe and efficient access to intelligent vehicles as well as intelligent …

LiDAR-based SLAM may easily fail in adverse weathers (eg, rain, snow, smoke, fog), while
mmWave Radar remains unaffected. However, current researches are primarily focused on …

Simultaneous Localization and Mapping (SLAM) is moving towards a robust perception age.
However, LiDAR-and visual-SLAM may easily fail in adverse conditions (rain, snow, smoke …

Millimeter wave (mmWave) radar excels in accurately estimating the distance, speed, and
angle of the signal reflectors relative to the radar. However, for diverse sensing applications …

LiDAR-and Visual-SLAM are prone to fail in smoke and fog environments, because LiDAR
and visual camera cannot penetrate smoke and fog. Fortunately, both 4D radar (x, y, z …

The integration of sensor data is crucial in the field of robotics to take full advantage of the
various sensors employed. One critical aspect of this integration is determining the extrinsic …

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

Most intelligent transportation and autonomous driving systems use the combination of
millimeter-wave (MMW) radar and camera to achieve strong perception, and correct extrinsic …