# Dense Visual Odometry Using Genetic Algorithm

arXiv: https://arxiv.org/abs/2311.06149 · HTML: https://mlsanity.com/paper/2311.06149/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Measuring robustness of Visual SLAM](https://mlsanity.com/paper/1910.04755/) (2019)
- [Continuous Direct Sparse Visual Odometry from RGB-D Images](https://mlsanity.com/paper/1904.02266/) (2019)
- [Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization](https://mlsanity.com/paper/1903.02046/) (2019)
- [Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)](https://mlsanity.com/paper/1407.0061/) (2014)

## Abstract (arXiv)

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.