We present a new, generic approach to the calibration of depth sensor intrinsics that requires only the ability to run SLAM. In particular, no specialized hardware, calibration target, or hand measurement is required. Essential to this approach is the idea that certain intrinsic parameters, identified here as myopic, govern distortions that increase with range. We demonstrate these ideas on the calibration of the popular Kinect and Xtion Pro Live RGBD sensors, which typically exhibit significant depth distortion at ranges greater than three meters. Making use of the myopic property, we show how to efficiently learn a discrete grid of 32,000 depth multipliers that resolve this distortion. Compared to the most similar unsupervised calibration work in the literature, this is a 100-fold increase in the maximum number of calibration parameters previously learned. Compared to the supervised calibration approach, the work of this paper means the difference between A) printing a poster of a checkerboard, mounting it to a rigid plane, and recording data of it from many different angles and ranges -a process that often requires two people or repeated use of a special easel -versus B) recording a few minutes of data from unmodified, natural environments. This is advantageous both for individuals who wish to calibrate their own sensors as well as for a robot that needs to calibrate automatically while in the field. • propose a general, unsupervised intrinsic calibration approach (CLAMS: Calibrating, Localizing, and Mapping, Simultaneously) appropriate for many different depth
For the intrinsic calibration of depth sensors, Teichmann et al. present a method called CLAMS (calibration, localization and mapping simultaneously) REF .
Unsupervised Intrinsic Calibration of Depth Sensors via SLAM.
Robotics: Science and Systems
Computer Science