Dense Visual Odometry Using Genetic Algorithm
This work addresses motion estimation for mobile robots or moving objects in static scenes, representing an incremental improvement over existing metaheuristic and classic methods.
The paper tackles camera motion estimation from RGB-D images by transforming it into a nonlinear least squares problem and proposes a genetic algorithm-based iterative method. The result shows improved efficiency over traditional methods, as evaluated using root mean square error on a large image set.
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.