Dense Visual Odometry Using Genetic Algorithm

Authors

  • Slimane Djema Department of Electronics, Saad Dahlab University, Blida, Algeria
  • Zoubir Abdeslem Benselama Department of Electronics, Saad Dahlab University, Blida, Algeria
  • Ramdane Hedjar Department of Computer Engineering, King Saud University, Riyadh, Saudi Arabia
  • Krabi Abdallah Department of Electronics, Saad Dahlab University, Blida, Algeria

Keywords:

camera motion, genetic Algorithm, RGB-D images, static scene, visual odometry

Abstract

Our work aims to estimate the camera motion mounted on the head of a mobile robot or a moving object from RGB-D images in a static scene. The problem of motion estimation is transformed into a nonlinear least squares function. Methods for solving such problems are iterative. Various classic methods gave an iterative solution by linearizing this function. We can also use the metaheuristic optimization method to solve this problem and improve results. In this paper, a new algorithm is developed for visual odometry using a sequence of RGB-D images. This algorithm is based on a genetic algorithm. The proposed iterative genetic algorithm searches using particles to estimate the optimal motion and then compares it to the traditional methods. To evaluate our method, we use the root mean square error to compare it with the based energy method and another metaheuristic method. We prove the efficiency of our innovative algorithm on a large set of images.

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Published

16.07.2023

How to Cite

Djema, S. ., Benselama, Z. A. ., Hedjar, R. ., & Abdallah, K. . (2023). Dense Visual Odometry Using Genetic Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 611–619. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3263

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

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