Binocular Vision-SLAM Using Improved SIFT Algorithm

Zhu, Daixian

doi:10.1109/iwisa.2010.5473273

Cited by 10 publications

(2 citation statements)

References 2 publications

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“…Using the known template image, we move the small squares to match with the small squares in another image, compare the position relationship of the two pixels, and calculate the actual depth of the object. Moreover, most of the matching algorithms should comply with four criteria: epipolar constraint, uniqueness constraint, parallax continuity constraint and sequence consistency constraint [13][14][15][16][17] In block stereo matching BM algorithm, the following similarity measurement function can be used for calculation:…”

Section: Camera Calibrationmentioning

confidence: 99%

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Three-dimensional Spatial Localization Based on Binocular Vsion

Zang

Zhang

Tian

et al. 2021

Preprint

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In order to solve the problems of low accuracy and unstable system performance existing in binocular vision alone, this paper proposes a threedimensional space recognition and positioning algorithm based on binocular stereo vision and deep learning algorithms. First, a binocular camera for Zhang Zhengyou calibrated by several adjustments, calibration error will eventually set at 0.10pixels best, select and SAD in block matching algorithm in the algorithm, the matching point of the search range reduction, mitigation data for subsequent experiments burden. Then input the three-dimensional spatial data calculated by using the binocular ”parallax” principle into the Faster R-CNN model for data training, extract and classify the target features, and finally realize real-time detection of the target object and its position coordinate information. The analysis of experimental data shows that when the best calibration error is selected and the number of data training is sufficient, the algorithm in this paper can effectively improve the quality of target detection. The positioning accuracy and target recognition rate are increased by about 3%-5%, and it can achieve faster fps.

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Section: Camera Calibrationmentioning

confidence: 99%

“…Common point feature-based algorithms include SIFT, SURF, ORB and so on. Researchers deeply study the extraction and matching of feature points and realize the accuracy and real-time performance of the algorithm [13][14][15][16][17]. In [18], a variety of vision schemes are proposed for different complex environments.…”

Section: Introductionmentioning

confidence: 99%