A study on reliability enhancement for laser and camera calibration

Shin, Yong-Deuk; Park, Jae‐Han; Bae, Ji‐Hun; Baeg, Moon-Hong

doi:10.1007/s12555-012-0112-3

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…Here Table 1 showed the standard deviations of evaluation function for the 21-group different scene in each independent experiment; the best extrinsic parameters calibration results are in the 10th experiment, the standard deviation of fitness function is only 0.2981, and the average of the fitness function is 0.3381, which is better than the nonlinear least square and nonlinear Gauss-Newton optimization methods for different constraints in [11]. Specifically, extrinsic parameters calibration is optimized by the nonlinear least square method for the first constraint (15); then these parameters are re-optimized by the nonlinear Gauss-Newton method for the second constraint (16) in [11]. In addition, the nonlinear least square and nonlinear Gauss-Newton methods are both utilized step by step for extrinsic parameters calibration process in [11].…”

Section: Pso For the Extrinsic Parameters Separated Calibrationmentioning

confidence: 97%

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Parameters Separated Calibration Based on Particle Swarm Optimization for a Camera and a Laser-Rangefinder Information Fusion

Zhou

2014

Mathematical Problems in Engineering

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Heterogeneous sensors fusion of a camera and a laser-rangefinder can greatly improve the environment perception ability, and its primary problem is the calibration of depth scan and image information. At first, the mapping relationship among world coordinate system, camera coordinate system, and image plane is discussed, and then the calibration of camera intrinsic parameters is achieved. Moreover, the intrinsic and extrinsic parameters separated calibration is presented for a camera and a laser-rangefinder, and the characteristic identification is adopted by two intersection calibration boards with a certain angel for fusion characters extraction. Furthermore, the particle swarm optimization is proposed for the extrinsic parameters estimation with different objectives, and the Gaussian elimination is utilized for the initial particle swarm. The simulation and real experimental results show that the standard deviation of calibration error in the 21-group experiments is decreased by 10.175%, and it also proves the accuracy and effectiveness of our approaches.

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Section: Pso For the Extrinsic Parameters Separated Calibrationmentioning

confidence: 97%

“…The most widely used model of camera is the typical pinhole model of camera [15]. The equation of the model is…”

Section: Spatial Coordinate Transformationmentioning

confidence: 99%

Parameters Separated Calibration Based on Particle Swarm Optimization for a Camera and a Laser-Rangefinder Information Fusion

Zhou

2014

Mathematical Problems in Engineering

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“…Here, the laser rangefinder coordinates system is ( , , ) t t t f x y z , ( , ) g u v are the coordinates of the optical image plane, and the target object coordinates in the world coordinate system are ( , , ) c c c h x y z . The most widely-used model of camera is the typical pinhole camera [16]. The equation of the model is as in (1):…”

Section: Spatial Coordinate Transformationmentioning

confidence: 99%

“…Firstly, the initial values of are optimized by the nonlinear least square method under the first constraint (16), and then the optimized is re-optimized by the nonlinear Gauss-Newton method under the second constraint (17). As for the optimized initial values solved by SVD, they can be optimized by the least square method with the constraint formula (16). The MatLab toolbox can provide the non-linear least square optimization function of optimset( ) and the result is = [1.07568368779805e-05, 2.51350684888328e-06, 1.163742235036e-05; -2.51987753199269e-07,8.9501764963208e-06, -0.0003675875774005; 1.61472210106329e-06, -6.79520649508e-06,-0.00119390634863761]…”

Section: The Parameters' Optimizationmentioning

confidence: 99%

Separated Calibration of a Camera and a Laser Rangefinder for Robotic Heterogeneous Sensors

Meng

Zuo

et al. 2013

International Journal of Advanced Robotic Systems

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The fusion of the heterogeneous sensors can greatly improve the environmental perception ability of mobile robots. And that the primary difficulty of heterogeneous sensors fusion is the calibration of depth scan information and plane image information for a laser rangefinder and a camera. Firstly, a coordinate transformation method from a laser rangefinder coordinates system to an optical image plane is given, and then the calibration of the camera's intrinsic parameters is achieved by “Camera Calibration Toolbox‘. Secondly, the intrinsic and extrinsic parameters are separated for calibration are proposed and compared, in which the characteristic parameters' identification is according to some characteristic points on the intersection line. Then Gaussian elimination is utilized for the initial value. Furthermore, the parameters' optimization using the non-linear least square and non-linear Gauss-Newton methods is devised for different constraints. Finally, the simulated and real experimental results demonstrate the reliability and effectiveness of extrinsic and intrinsic parameters' separated calibration, meanwhile, the real-time analysis is achieved for robotic multi-sensor fusion.

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Single Laser Bidirectional Sensing for Robotic Wheelchair Step Detection and Measurement

Mamun

Lam

Kobayashi

et al. 2017

Intelligent Computing Methodologies

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A study on reliability enhancement for laser and camera calibration

Cited by 11 publications

References 4 publications

Parameters Separated Calibration Based on Particle Swarm Optimization for a Camera and a Laser-Rangefinder Information Fusion

Parameters Separated Calibration Based on Particle Swarm Optimization for a Camera and a Laser-Rangefinder Information Fusion

Separated Calibration of a Camera and a Laser Rangefinder for Robotic Heterogeneous Sensors

Single Laser Bidirectional Sensing for Robotic Wheelchair Step Detection and Measurement

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