Targetless calibration of thermal camera and LiDAR for sensor fusion SLAM

Abstract

In the Simultaneous Localization And Mapping technology for autonomous driving robots, the camera provides semantic information, and the LiDAR provides accurate position for the surrounding environ- ment. However, the camera image is sensitive to external light, such as at night and a sudden changing of light source, so fatal problems such as tracking loss occur. There are many attempts to replace con- ventional cameras with Thermal cameras in SLAM area. For the sensor fusion of Thermal camera and LiDAR data, it is very important to accurately calculate the transformation between the coordinate sys- tems defined based on each sensor. This is called calibration. Conventionally, chessboard target is used for calibration of a camera and LiDAR. The calibration is performed by deriving the three-dimensional coordinates of the corner points through the triangulation and matching them with the points obtained from the LiDAR. For this reason, a clear corner point of the target is a very important factor in camera calibration. However, unlike cameras that measure light in the visible range, Thermal cameras measure infrared light emitted from the surface of an object. This infrared radiation is related to the temperature of the object’s surface, blur in thermal image is inevitable due to heat transfer. Therefore, it is difficult and inaccurate to find a clear corner point in the chessboard-shaped target. To overcome this problem,this thesis proposes a targetless calibration method beteween Thermal camera and LiDAR.