A Noise-Tolerant Algorithm for Robot-Sensor Calibration Using a Planar Disk of Arbitrary 3-D Orientation

In a 3-D scanning task, a robot-sensor system controls a robotic arm to move a laser sensor. In order to align the coordinate system of the robotic arm and laser sensor, prior calibration is required to derive the transformation between both coordinate systems. This paper proposes a new calibration method in three steps: manual data collection, sensing data calculation, and transformation solution. First, at least four data are required to be collected by the user. The sensing data are then calculated from the collected data and adopted to provide the desired transformation. The proposed algorithm has two features: arbitrary placement of planar disk and noise tolerant. Using a planar disk, the algorithm will automatically derive the angular relationship between the disk and the sensor plane, enabling arbitrary orientation placement. Noise tolerant is guaranteed by fitting ellipses during the sensing data calculation and using a single set of sensing data in transformation solution. Experiments and comparisons are given to demonstrate the efficiency of the proposed calibration algorithm.

Note to Practitioners—This paper was motivated by the problem of calibrating a laser sensor and a positioning device (robot arm, CMM, etc.) in a robust and fast manner. Specifically, the calibration is to derive the transformation by aligning the sensors coordinate system to the positioning devices coordinate system. The proposed calibration procedure consists of two parts: manual data collection and automatic transformation calculation. During manual data collection, users only need to select four different data; whereby each data contains of two positions with the same orientation. Then, the desired transformation will be derived automatically. The calibration is designed in an efficient and robust way whereby: 1) data collection is done using a simple planar disk placed in arbitrary orientations; 2) minimum human interaction required; 3) tolerant to noise in the sensor data; and 4) easy implementation by following a proven and standard protocol.