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- W4301184496 abstract "Aiming at the problem that the vocabulary tree in the traditional bag-of-words model algorithm is not strong enough to represent the scene, which will affect the performance of the loop detection module in the visual SLAM system and produce accumulated errors, this paper proposes a slam loop detection algorithm based on the optimized bag-of-words model, improves the clustering algorithm for generating offline vocabulary tree, and uses the improved K-Means algorithm to build the vocabulary tree; At the same time, the image feature data of the experimental scene is used to update the vocabulary tree to improve the representation ability of the vocabulary tree. In this paper, the data sets of CityCenter, NewCollege, KITTI00 and KITTI06 are compared and analyzed experimentally. Compared with the experiments before and after updating the vocabulary tree, when the accuracy is 100%, the maximum recall rate of the algorithm on the four data sets is improved by 11.28%, 24.73%, 16.58% and 17.73% respectively; Then, compared with the five classical algorithms, the maximum recall rates of the four data sets are 0.39%, 11.27%, 2.39% and 22.67% higher than the best results of the five algorithms, respectively. The improved algorithm reduces the processing time, and the experimental results verify the correctness and effectiveness of the improved algorithm." @default.
- W4301184496 created "2022-10-04" @default.
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- W4301184496 date "2022-08-19" @default.
- W4301184496 modified "2023-10-18" @default.
- W4301184496 title "SLAM Loop Detection Algorithm Based on Improved Bag-of-Words Model" @default.
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- W4301184496 doi "https://doi.org/10.1109/prai55851.2022.9904167" @default.
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