  The homogeneous transformation between a LiDAR and monocular camera is
required for sensor fusion tasks, such as SLAM. While determining such a
transformation is not considered glamorous in any sense of the word, it is
nonetheless crucial for many modern autonomous systems. Indeed, an error of a
few degrees in rotation or a few percent in translation can lead to 20 cm
translation errors at a distance of 5 m when overlaying a LiDAR image on a
camera image. The biggest impediments to determining the transformation
accurately are the relative sparsity of LiDAR point clouds and systematic
errors in their distance measurements. This paper proposes (1) the use of
targets of known dimension and geometry to ameliorate target pose estimation in
face of the quantization and systematic errors inherent in a LiDAR image of a
target, and (2) a fitting method for the LiDAR to monocular camera
transformation that fundamentally assumes the camera image data is the most
accurate information in one's possession.
