Abstract
In this paper we describe a method for the automatic self-calibration of a 3D laser sensor. We wish to acquire crisp point clouds and so we adopt a measure of crispness to capture point cloud quality. We then pose the calibration problem as the task of maximizing point cloud quality. Concretely, we use Rényi Quadratic Entropy to measure the degree of organization of a point cloud. By expressing this quantity as a function of key unknown system parameters, we are able to deduce a full calibration of the sensor via an online optimization. Beyond details on the sensor design itself, we fully describe the end-to-end intrinsic parameter calibration process and the estimation of the clock skews between the constituent microprocessors. We analyse performance using real and simulated data and demonstrate robust performance over 30 test sites.
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