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- W4313484789 abstract "Monocular visual odometry consists of the estimation of the position of an agent through images of a single camera, and it is applied in autonomous vehicles, medical robots, and augmented reality. However, monocular systems suffer from the scale ambiguity problem due to the lack of depth information in 2D frames. This paper contributes by showing an application of the dense prediction transformer model for scale estimation in monocular visual odometry systems. Experimental results show that the scale drift problem of monocular systems can be reduced through the accurate estimation of the depth map by this model, achieving competitive state-of-the-art performance on a visual odometry benchmark." @default.
- W4313484789 created "2023-01-06" @default.
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- W4313484789 date "2022-10-18" @default.
- W4313484789 modified "2023-10-18" @default.
- W4313484789 title "Dense Prediction Transformer for Scale Estimation in Monocular Visual Odometry" @default.
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- W4313484789 doi "https://doi.org/10.1109/lars/sbr/wre56824.2022.9995735" @default.
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