遺伝的アルゴリズムを用いた高密度視覚オドメトリ
Dense Visual Odometry Using Genetic Algorithm
RGB-D画像列からカメラの動きを推定する視覚オドメトリ問題を遺伝的アルゴリズムで解き、従来手法より高精度に推定できることを示した研究。
著者: Slimane Djema, Zoubir Abdeslem Benselama, Ramdane Hedjar, Krabi Abdallah
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
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.