A Markerless Entropy-Based Method for Data-Driven LIDAR-GNSS/INS Calibration on a UAV

When using a GNSS/INS sensor to provide poses to combine LiDAR scans over time, knowing the extrinsic calibration between this GNSS/INS sensor and the LiDAR device is essential. In this work we present a new calibration method that directly optimises an entropy-based loss function, starting from a decent, but suboptimal calibration, e.g. obtained using simple measurement tools such as a ruler and a protractor. Our method does not require placing any markers, nor imposes restrictions on the types of scenes. In particular, we do not rely on plane detection and planar features. Our experiments demonstrate that we can compensate for initial rotational errors up to the order of 4 degrees.