Study On Key Problems Of Processing Point Cloud Of Dynamic Subsidence Area From Terrestrial Laser Scanner

In the study of mining subsidence, aiming at getting the influence degree ofunderground mining so as to guide the protection of constructions on the ground andthe production of underground, long term monitoring of the ground in the influencedarea is commonly taken by deploying observation station on the ground. Afterintroducing the terrestrial laser scanner(TLS) into the study of mining subsidence, itsolved a lot of problems in traditional observation station, such as inadequacy of datavolume and long period of observation etc. Nevertheless, there are still several keyproblems, which demand prompt solution, in the application of this technique inmining subsidence monitoring. Combining with the advantage of abundant data fromTLS, this thesis proposes the model of point cloud rectification and extractionalgorithms of multiple subsidence information by means of proposing new algorithmsor improving existing algorithms which is proved to be efficient.Combining with the principle of terrestrial laser scanning, this thesis deduced thegeometrical relationship between scanner inclination and errors of angles acquired,then the errors of angles acquired caused by scanner inclination were acquired.Afterwards, according to the mathematical relationship between the point coordinatesand angles acquired, data rectification was taken by add errors of angels acquired tothe angles acquired so as to eliminate the influence of scanner inclination on the pointcoordinates.Calculate the horizontal displacement of characteristic points to denote thedisplacement of the ground and compare with the horizontal displacement acquired bytotal station with high precision to evaluate the reliability of the algorithms.Stepwise cracks were detected wavelet transform. After tracing the suddenchanges from different scan lines, the stepwise steps were acquired. Point clouds withplanar cracks were firstly pictorialized to be point cloud images and then densified tomake the crack stand out.Lastly, take approaches of edge detection to extract cracksfrom the point cloud images and apply the algorithms into engineer practice toevaluate the reliability of the approaches compare to the locations of cracks acquiredby total station with high precision.