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About Visual Global Localization With A Hybrid WNN-CNN Approach
The document proposes a hybrid approach using a Weightless Neural Network (WNN) and Convolutional Neural Network (CNN) for visual global localization. First, a WNN performs place recognition on a live image to identify the most similar past image and its pose. Then, a CNN takes the live and past images as input to estimate their relative camera pose. Experimental results show the combined WNN-CNN approach can localize a vehicle with a mean error of 1.20m, compared to 1.12m for visual SLAM and 0.37m for GPS.
Detailed Information
| Author: | ['DANIKA SHRIVASTAVA 20BCE2314'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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