Maximum likelihood remission calibration for groups of heterogeneous laser scanners

Laser range scanners are commonly used in mobile robotics to enable a robot to sense the spatial configuration of its environment. In addition to the range measurements, most scanners provide remission values, representing the intensity of the returned light pulse. These values add a visual component to the measurement and can be used to improve reasoning on the data. Unfortunately, a remission value does not directly tell us how bright a measured surface is in the infrared spectrum. Rather, it varies with respect to the incidence angle and the range at which it was measured. In addition, multiple scanners typically do not agree upon the values of a certain surface. In this paper, we present a calibration method for remission values of multiple laser scanners considering dependencies in range, incidence angle of the measured surface, and the respective scanner unit. Our system learns the calibration parameters based on a set of registered point clouds. It uses a graph optimization scheme to minimize the error between different measurements, so that all involved scanners yield consistent reflection values, independent of the perspective from which the corresponding surface is observed.