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Depth cameras, typically in RGB-D configurations, are common devices inmobile robotic platforms given their appealing features: high frequency andresolution, low price and power requirements, among others. These sensors maycome with significant, non-linear errors in the depth measurements thatjeopardize robot tasks, like free-space detection, environment reconstructionor visual robot-human interaction. This paper presents a method to calibratesuch systematic errors with the help of a second, more precise range sensor, inour case a radial laser scanner. In contrast to what it may seem at first, thisdoes not mean a serious limitation in practice since these two sensors areoften mounted jointly in many mobile robotic platforms, as they complement welleach other. Moreover, the laser scanner can be used just for the calibrationprocess and get rid of it after that. The main contributions of the paper are:i) the calibration is formulated from a probabilistic perspective through aMaximum Likelihood Estimation problem, and ii) the proposed method can beeasily executed automatically by mobile robotic platforms. To validate theproposed approach we evaluated for both, local distortion of 3D planarreconstructions and global shifts in the measurements, obtaining considerablymore accurate results. A C++ open-source implementation of the presented methodhas been released for the benefit of the community.

Intrinsic Calibration of Depth Cameras for Mobile Robots using a Radial Laser Scanner