Accurate evaluation of sensitivity for calibration between a LiDAR and a panoramic camera used for remote sensing

Abstract. Computer-based reconstruction models can be used to approximate urban environments. These models are usually based on several mathematical approximations and the usage of different sensors, which implies dependency on many variables. The sensitivity analysis presented in this paper is used to weigh the relative importance of each uncertainty contributor into the calibration of a panoramic camera–LiDAR system. Both sensors are used for three-dimensional urban reconstruction. Simulated and experimental tests were conducted. For the simulated tests we analyze and compare the calibration parameters using the Monte Carlo and Latin hypercube sampling techniques. Sensitivity analysis for each variable involved into the calibration was computed by the Sobol method, which is based on the analysis of the variance breakdown, and the Fourier amplitude sensitivity test method, which is based on Fourier’s analysis. Sensitivity analysis is an essential tool in simulation modeling and for performing error propagation assessments.

[1]  Syamal K. Sen,et al.  QUASI-VERSUS PSEUDO-RANDOM GENERATORS: DISCREPANCY, COMPLEXITY AND INTEGRATION-ERROR BASED COMPARISON , 2006 .

[2]  Stefano Tarantola,et al.  Sensitivity analysis of spatial models , 2009, Int. J. Geogr. Inf. Sci..

[3]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[4]  Wei Sun,et al.  An empirical evaluation of factors influencing camera calibration accuracy using three publicly available techniques , 2006, Machine Vision and Applications.

[5]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Roger D. Braddock,et al.  The New Morris Method: an efficient second-order screening method , 2002, Reliab. Eng. Syst. Saf..

[7]  João Felipe Coimbra Leite Costa,et al.  Improving the Efficiency of the Sequential Simulation Algorithm using Latin Hypercube Sampling , 2005 .

[8]  James W. Jones,et al.  Working with Dynamic Crop Models , 2014 .

[9]  Janne Heikkilä,et al.  Geometric Camera Calibration Using Circular Control Points , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  I. Sobol.  On the distribution of points in a cube and the approximate evaluation of integrals , 1967 .

[11]  Raymond H. Myers,et al.  Response Surface Methodology--Current Status and Future Directions , 1999 .

[12]  Alle-Jan van der Veen,et al.  Multisource Self-Calibration for Sensor Arrays , 2009, IEEE Transactions on Signal Processing.

[13]  Jon C. Helton,et al.  A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling , 2005, Reliab. Eng. Syst. Saf..

[14]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[15]  Flavio Cannavó,et al.  Sensitivity analysis for volcanic source modeling quality assessment and model selection , 2012, Comput. Geosci..

[16]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[17]  D. Cacuci,et al.  Applications to large-scale systems , 2005 .

[18]  José-Joel González-Barbosa,et al.  Error propagation and uncertainty analysis between 3D laser scanner and camera , 2014, Robotics Auton. Syst..

[19]  Agostino Martinelli,et al.  Vision and IMU Data Fusion: Closed-Form Solutions for Attitude, Speed, Absolute Scale, and Bias Determination , 2012, IEEE Transactions on Robotics.