Laser beams-based localization methods for Boom-type roadheader using underground camera non-uniform blur model

The efficiency of automatic underground tunneling is significantly depends on the localization accuracy and reliable for the Boom-type roadheader. In comparison with other underground equipment positioning methods, vision-based measurement has gained attention for its advantages of noncontact and no accumulated error. However, the harsh underground environment, especially the geometric errors brought by the vibration of the machine body to the underground camera model, has a certain influence on the accuracy and stability for the vision-based underground localization. In this paper, a laser beams-based localization methods for the machine body of Boom-type roadheader is presented, which can tackle the dense-dust, low illumination environment with the stray lights interference. Taking mining vibration into consideration, an underground camera non-uniform blur model that incorporate the two-layer glasses refraction effect was established to eliminate vibration errors. The blur model explicitly reveals the change of imaging optical path under the influence of vibration and double layer explosion-proof glass. On the basis of this, the underground laser beams extraction and positioning are presents, which is with well environmental adaptability, and the improved 2P3L (two-points-three-lines) localization model from line correspondences are developed. Experimental evaluation are designed to verify the performance of the proposed method, and the deblurring algorithm is investigated and evaluated. The results show that the proposed methods is effective to restore the blurred laser beams image that caused by the vibration, and can meet the precision need of roadheader body localization for roadway construction in coal mine

Infrared LEDs-based pose estimation with underground camera model for Boom-type roadheader in coal mining

Accurate and reliable pose estimation of boom-type roadheader is of great importance in order to maintain the efficiency of automatic coal mining. The stability and accuracy of conventional measurement methods are difficult to be guaranteed on account of vibration noise, magnetic disturbance, electrostatic interference and other factors in underground environment. In this paper a vision-based non-contact measurement method for cutting-head pose estimation is presented, which deploy a 16-point infrared LED target on the boom-type roadheader to tackle the low illumination, high dust and complicated background. By establishing monocular vision measurement system, the cutting-head pose is estimated through processing the LED target images obtained from an explosion-proof industrial camera mounted on the roadheader. After analyzing the measurement mechanism, an underground camera model based on the equivalent focal length is built to eliminate refraction errors caused by the two layer glasses for explosion-proof and dust removal glasses. Then the pose estimation processes, including infrared LEDs feature points extraction, spot center location, improved P4P method based on dual quaternions, are carried out. The influence factors of cutting-head pose estimation accuracy is further studied by modeling, and the error distribution of the main parameters is investigated and evaluated. Numerical simulation and experimental evaluation are designed to verify the performance of the proposed method. The results show that the pose estimation error is in line with the numerical prediction, achieving the requirements of cutting-head pose estimation in underground roadway construction in coal mine