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.

本研究通过使用基于遗传算法的迭代遗传算法来估计以静态场景中的 RGB-D 图像为基础的移动机器人或移动物体上的头部摄像机运动，并通过与传统方法以及另一种元启发式方法的均方根误差比较来证明了该算法的高效性。