Summary of Pole-based Vehicle Localization with Vector Maps: a Camera-lidar Comparative Study, by Maxime Noizet (heudiasyc) et al.
Pole-based Vehicle Localization with Vector Maps: A Camera-LiDAR Comparative Study
by Maxime Noizet, Philippe Xu, Philippe Bonnifait
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a solution for accurate localization in urban environments, where infrastructure like buildings and bridges can interfere with Global Navigation Satellite Systems (GNSS) and inertial navigation systems. By georeferencing features like traffic signs, lights, and streetlights in vector maps, these poles can be used within a localization filter that includes detection pipeline and data association method. The paper introduces a real-time method for camera-based pole detection using a lightweight neural network trained on automatically annotated images. It evaluates the efficacy of these approaches in terms of localization accuracy and highlights the high accuracy of the vision-based approach in open road conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists work to make autonomous vehicles more accurate at knowing where they are. They want to overcome challenges caused by buildings and bridges that can confuse GPS systems. To do this, they use special maps with details about features like traffic signs and streetlights. These features can help a car know its location even when GPS is confused. The researchers develop a new way to detect these features using cameras and computer algorithms. They test their approach in real-world scenarios and show that it is very accurate. |
Keywords
» Artificial intelligence » Neural network
