  Light Detection and Ranging (LIDAR) sensors play an important role in the
perception stack of autonomous robots, supplying mapping and localization
pipelines with depth measurements of the environment. While their accuracy
outperforms other types of depth sensors, such as stereo or time-of-flight
cameras, the accurate modeling of LIDAR sensors requires laborious manual
calibration that typically does not take into account the interaction of laser
light with different surface types, incidence angles and other phenomena that
significantly influence measurements. In this work, we introduce a physically
plausible model of a 2D continuous-wave LIDAR that accounts for the
surface-light interactions and simulates the measurement process in the Hokuyo
URG-04LX LIDAR. Through automatic differentiation, we employ gradient-based
optimization to estimate model parameters from real sensor measurements.
