In the outdoor dynamic scene, the current Monocular Visual odometry methods of simultaneous localization and mapping system (SLAM) have Low utilization of image information, and they are not enough to meet the problem of stable image matching tracking. An information fusion method combining feature points and direct method gray matching method is proposed, which not only has no serious feature loss when demanding in fast motion, but also reduces the dependence on gray-scale invariant hypothesis. The proposed method also has better stability in the scenes with insufficient texture information, and the method reduces the dependence on gray-scale invariant hypothesis. Experimental results show that our novel front-end visual odometry simplifies the feature points, enriches the front-end utilization information, improves the pixel information of the image, and improves the feature points loss and excessive dependence on the intensity. The average real-time frame rate is around 32Hz, meeting the requirements of real-time performance. In the large-scale scene of the KITTI visual odometry datasets, the jitter effect of the dynamic scene is weakened, and the root mean square error of the positioning is 1. 79m, The average root mean square error of the method is reduced to 22.77% of ORB-SLAM, 26.48% of Semi-Direct Monocular Visual Odometry(SVO),and 37.68% of Large-scale direct SLAM(LSD).
A method of Monocular Visual Odometry Combining Feature points and Pixel Gradient for Dynamic Scene
2020-05-01
203761 byte
Conference paper
Electronic Resource
English
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