Camera calibration using a genetic algorithm

Abstract

An autonomous robot will have to detect moving obstacles online before it can plan its collision-free path, while navigating in a dynamic environment. The robot collects information about the environment with the help of a camera and determines the inputs for its motion planner through image analysis. The present article deals with issues related to camera calibration and online image processing. The problem of camera calibration is treated as an optimization problem and solved using a genetic algorithm so as to achieve minimum distorted image plane error. The calibrated vision system is then utilized for the detection and identification of the objects by analysing the images collected at regular intervals. For image processing, five different operations, such as median filtering, thresholding, perimeter estimation, labelling and size filtering, have been carried out. To show the effectiveness of the developed camera-based vision system, inputs of the motion planner of a navigating robot are calculated for two different cases. It is observed that online detection of the shapes and configurations of the obstacles is possible by using the vision system developed.

Self-calibration of vision parameters via genetic algorithms with simulated binary crossover and laser line projection

“…[8][9][10][11][12][13][14][15][16] Thus, the traditional GA depends on external references to achieve the calibration. Therefore, the traditional GA uses known references to calibrate the vision parameters.…”

“…5 Thus, optical methods such as laser line and point projection calibrate the vision parameters via GAs to perform three-dimensional (3-D) vision. 12 Another GA performs the calibration by minimizing a cost function, which is deduced from the geometry of known points in the image plane. 8,9 Usually, the GA determines the vision parameters by means of an objective function, which is deduced from known references and image processing.…”

Simultaneous filter tuning and calibration of the camera and inertial measurement unit camera for a vision inertial navigation system

“…Xing et al [17] presented an enhanced GA that can definitely resolve the calibration of a stereo camera. In 2008, Hui and Pratihar [18] changed the problem of camera calibration to an optimisation issue and solved using a GA to achieve the minimum error of distorted image plane. The differential evolution is combined with particle swarm optimisation algorithm and is used for calibrating the camera variables effectively by Deng et al [19].…”

Optimal Pattern Synthesis of Thinned and Non-Uniformly Excited Concentric Circular Array Antennas using Hybrid GSA-PSO Technique

“…Thus, it is necessary to develop an efficient and robust optimization method. There are various evolutionary optimization techniques such as genetic algorithm (GA) [26][27][28][29], simulated annealing (SA) [30], gravitational search algorithm (GSA) [31][32][33][34], particle swarm optimization (PSO) [35][36][37][38][39][40] etc. for optimization of complex, discontinuous and non-differentiable array factor of the antenna array.…”