Calibration of Inverse Perspective Mapping for a Humanoid Robot

This paper proposes a method to calibrate the model used for inverse perspective mapping of humanoid robots. It aims at providing a reliable way to determine the robot’s position given the known objects around it. The position of the objects can be calculated using coordinate transforms applied to the data from the robot’s vision device. Those transforms are dependent on the robot’s joint angles (such as knee, hip) and the length of some components (e.g. torso, thighs, calves). In practice, because of the sensitivity of the transforms with respect to the inaccuracies of the mechanical data, this calculation may yield errors that make it inadequate for the purpose of determining the objects’ positions. The proposed method reduces those errors using an optimization algorithm that can find offsets that can compensate those mechanical inaccuracies. Using this method, a kid-sized humanoid robot was able to determine the position of objects up to 2 m away from the itself with an average of 3.4 cm of error.