Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking
This work addresses calibration and accuracy issues in LIDAR sensors for autonomous robots, though it appears incremental as it builds on existing simulation and optimization techniques.
The paper tackles the problem of accurately modeling LIDAR sensors, which require laborious manual calibration and often ignore surface-light interactions, by introducing a physically plausible simulation model for a 2D continuous-wave LIDAR and using gradient-based optimization to estimate parameters from real measurements.
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.