For the application of industrial robots, positioning accuracy is a significant indicator of their performance. Aiming at the issues of low positioning accuracy, the calibration techniques are employed to obtain the real kinematic parameters for effectively enhancing its accuracy. However, in practical scenarios, for the restriction of the robot workspace, the samples collected during the robot calibration process cannot cover the entire space of the entire space, resulting in an impact on data integrity. To address the above issues, we develop a calibrator integrating the MCS method (measurement configurations selection) and LM algorithm (Levenberg-Marquardt) with a spatial line constraint (LMLC), which contains three-fold: a) selecting a set of most representative measurement configurations according to the observability index for enhancing the stability of calibration results; b) develop an LM algorithm with line constraint to solve the problem of spatial restriction of robot sampling; c) presenting a robot calibrator that combines MCS and LMLC for efficiently improving the robot calibration accuracy. Experiments illustrate that the MCS-LMLC calibrator outperforms state-of-the-art calibrators on an industrial robot’s calibration accuracy and computational efficiency.