Modelo para evaluación de la exactitud de escáner láser terrestre - TLS

William Benigno Barragán; Lorena Lizeth Herrera De la Rosa; Lilia Edith Aparicio Pico

doi:10.22335/rlct.v12i1.1019

Autores

William Benigno Barragán Universidad Distrital Francisco José de Caldas https://orcid.org/0000-0001-9851-7857
Lorena Lizeth Herrera De la Rosa Universidad Distrital Francisco José de Caldas
Lilia Edith Aparicio Pico Universidad Distrital Francisco José de Caldas

DOI:

https://doi.org/10.22335/rlct.v12i1.1019

Palavras-chave:

Scanner a laser terrestre, avaliação de precisão, análise de precisão, precisão TLS

Resumo

O presente trabalho determina a precisão da nuvem de pontos capturada por um scanner a laser terrestre (TLS), com o objetivo de desenvolver um modelo estatístico que permita avaliar a precisão do scanner terrestre. Este modelo é baseado em experimentação, com variação de parâmetros de referência em medições diretas. Os dados são obtidos através de procedimentos que incluem a captura de informações sobre diferentes tipos de distâncias, ângulos e superfícies (alvo). Para dar força à validade do modelo obtido, os dados obtidos são comparados com relação a outros adquiridos com uma estação topográfica total. O desenvolvimento matemático e estatístico do modelo usa a teoria do design do experimento, onde as medidas feitas em cada uma das varreduras são independentes uma da outra, assim como cada tipo de alvo. Propor a implementação da análise do modelo linear geral (MLG), para ajustar uma expressão matemática ao erro médio (distâncias euclidianas entre as coordenadas observadas e as teóricas). O modelo desenvolvido permitiu determinar a precisão dos dados adquiridos pelo laser terrestre, estabelecendo que para cada metro adicional à distância do scanner em relação à superfície de estudo, o erro médio esperado aumentará entre 0,033% e 1,5%.

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Biografia do Autor

William Benigno Barragán, Universidad Distrital Francisco José de Caldas

Profesor Asociado.
Lorena Lizeth Herrera De la Rosa, Universidad Distrital Francisco José de Caldas

Msc Ciencias de la Información y las comunicaciones.
Lilia Edith Aparicio Pico, Universidad Distrital Francisco José de Caldas

PhD. en Ciencias Técnicas. Msc. Teleinformática, Especialista en Gerencia de Proyectos Educativos. Profesor Titular.

Referências

Al-Durgham, K., Habib, A., & Kwak, E. (2013). RANSAC approach for automated registration of terrestrial laser scans using linear features. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W2, 13-18.

Aoki, R., Aoki, S., & Miyamoto, R. (2017). Key point localization for 3d model generation from facial illustrations using SURF and color features (pp. 55-56). In IEEE 7th International Conference on Consumer Electronics-Berlin (ICCE-Berlin).

Barragán, W., & Escobar, K. (2017). Obtención de parámetros óptimos en la clasificación de nubes de puntos LiDAR, a partir de sensores aerotransportados. Revista Avances Investigación en Ingeniería, 14(1). Bogotá: Universidad Libre.

Barragán, W., & Martínez A. (2015). Generación automática de cartografía para edificaciones, utilizando fotografías digitales aéreas verticales de alta resolución espacial y nubes de puntos. Revista de Ingeniería, 42. Bogotá: Universidad de los Andes.

Chiba, A., & Masuda, H. (2016). Reconstruction of polygonal prisms from point-clouds of engineering facilities. Journal of Computational Design and Engineering, 3, 322-329.

Cohen, J. (1962). The statistical power of abnormal-social psychological research: A review. Abnorm. Soc. Psychol., 65(3), 145-153.

Date, H., Yokoyama, T., & Sugawara, T. (2018). Efficient registration of laser-scanned point clouds of bridges using linear features. International Journal of Automation Technology, 328-338.

Dong, J., Cai, Z., & Du, S. (2016). Improvement of affine iterative closest point algorithm for partial registration. IET Comput. Vis., 11(2), 135-144.

Enderlein, G., McCullagh, P., & Nelder, J. A. (1987). Generalized linear models. Chapman and Hall. London, New York, 1983. Biometrical Journal, 29(2), 206-206 [sic].

Faro Focus. (2018) | FARO SPAIN, S.L.U. [en línea]. Accedido: 24-may-2018. Disponible en https://www.faro.com/es es/productos/construccion-bim-cim/faro-focus.

Forstner, W., & Khoshelham, K. (2017). Efficient and accurate registration of point clouds with plane to plane correspondences (pp. 2165-2173). In Proceedings of the IEEE International Conference on Computer Vision.

Ge, X. (2017). Automatic markerless registration of point clouds with semantic-keypoint-based 4 points congruent sets. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 344-357.

Gressin, A., Mallet, C., Demantké, J., & Da, N. (2013). Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge. ISPRS Journal of Photogrammetry and Remote Sensing, 79, 240-251.

Kaasalainen, S., Jaakkola, A., Kaasalainen, M., Krooks, A., & Kukko, A. (2011). Analysis of incidence angle and distance effects on terrestrial laser scanner intensity: Search for correction methods. Journal Remote Sensing ISPRS, 59-68.

Lemonte, A. J., & Vanegas, L. H. (2005). Una comparación entre la inferencia basada en las estadísticas de Wald y razón de verosimilitud en los modelos probabilísticos. Rev. Colombiana de Estadística, 28, 77-97.

Li, W., & Song, P. (2015). A modified ICP algorithm based on dynamic adjustment factor for registration of point cloud and CAD model. Pattern Recognit., 65, 88-94.

Lichti, D. D., & Licht, M. G. (2006). Experiences with terrestrial laser scanner modelling and accuracy assessment. IAPRS, XXXVI. Germany: Dresden.

Little, T. D. (2013). The Oxford Handbook of Quantitative Methods in Psychology, vol. 2: Statistical analysis. OUP USA.

Lohani, B., & Sasidhaan, S. (2017). An evaluation of intensity augmented ICP for terrestrial LiDAR data registration. Journal of Geomatics, 11(2), 139-148.

López, G. E., & Ruiz, S. M. (2011). Análisis de datos con el modelo lineal generalizado. Una aplicación con R. Rev. Esp. Pedagog., 69(248), 59-80.

Lu, J., Wang, W., & Guo, C. (2018). Point cloud registration based on CPD algorithm (pp. 8235-8240). 37th Chinese Control Conference (CCC) IEEE.

Masuda, H., & Tanaka, I. (2010). As-built 3D modeling of large facilities based on interactive feature editing. Computer-Aided Design and Applications, 7(3), 349-360.

Mellado, N., Aiger, D., & Mitra, N. J. (2014). Super 4pcs fast global point cloud registration via smart indexing. Computer Graphics Forum, 33(5), 205-215.

Ozendi, M., Akca, D., & Topan, H. (2017). A generic point error model for TLS derived point clouds. In: Proceedings of SPIE, 10332, 103320J.

Poreba, M., & Goulette, F. (2015). A robust linear featurebased procedure for automated registration of point clouds. Sensors, 15, 1435-1457.

Ramos, L., Marchamalo, M., & Martínez, R. (2017). Estimating and plotting TLS midrange precisions in field conditions: Application to dam monitoring. International Journal of Civil Engineering, 15, 299-307.

Ramos, L., Marchamalo, M., Rejas, J. G., & Martínez, R. (2015). Aplicación del láser escáner terrestre (TLS) a la modelización de estructuras: precisión, exactitud y diseño de la adquisición de datos en casos reales. Informes de la Construcción, 67(538): e074, doi: http://dx.doi.org/10.3989/ic.13.103.

Saeedi, S., Trentini, M., Seto, M., & Li, H. (2016). Multiple robot simultaneous localization and mapping: A review. Journal of Field Robotics, 33(1), 3-46.

Soudarissanane, S., Lindenbergh, R., & Menenti, M. (2011). Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 389-399.

Soudarissanane, S., Lindenbergh, R., Menenti, M. (2009). Incidence angle influence on the quality of terrestrial laser scanning points. Proceedings of ISPRS Workshop Laser Scanning, 183-188.

Sumi, T., Date, H., & Kanai, S. (2018). Multiple TLS point cloud registration based on point projection images. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1083-1090.

Takai, S., Date, H., & Kanai, S. (2013). Accurate registration of MMS point clouds of urban areas using trajectory. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W2, 277-282.

Theiler, P. W., Wegner, J. D., & Schindler, K. (2013). Markerless point cloud registration with keypoint-based 4-points congruent sets. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W2, 283-288.

Voegtle, A., & Ruoming, R. (2009). Effects on the measurements of the terrestrial laser scanner Hds 6000 (Leica) caused by different object materials. Institute of Photogramm. Remote Sensing (IPF). Germany: Universitaet Karlsruhe (TH).

Voegtle, A., & Schwab A. (2008). Influences of different materials on the measurements of a terrestrial laser scanner (TLS). Proc. of the XXI Congress, the International Society for Photogrammetry and Remote Sensing, ISPR.

Wujanz, D., Burger, M., & Mettenleiter, M. (2017). An intensity-based stochastic model for terrestrial laser scanners. ISPRS Journal of Photogrammetry and Remote Sensing, 125, 146-155.

Xu, Y., Boerner, R., & Yao, W. (2017). Automated coarse registration of point clouds in 3D urban scenes using voxel based plane constraint. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, IV-2/W4, 185-191.

Yang, B., Dong, Z., & Liang, F. (2016). Automatic registration of large-scale urban scene point clouds based on semantic feature points. ISPRS Journal of Photogrammetry and Remote Sensing, 113, 43-58.

Yoshimura, R., Date, H., & Kanai, S. (2016). Automatic registration of MLS point clouds and SfM meshes of urban area. Geo-spatial Information Science, 19(3), 171-181

Zhou, Y., Cui, M., & Yang, L. (2009). Application of 3D laser scanner in topographic change monitor and analysis (pp. 4-382-4-385). In 9th International Conference on Electronic Measurement Instruments. ICEMI.