CMU 16-822 Geometry Vision Project - Updating HD map information with data from smartphones.
High Definition (HD) maps are an important commodity for modern self-driving car companies as they are useful to precisely localize in the city. Modern datasets such as the Argoverse dataset [1], annotate the driveable areas in the map data. However, the driveable areas need to be constantly updated with changing conditions of a city. Construction zones can pop up, accidents can occur, or weather can affect road conditions — all of which can cause lane closures or other obstructions to a routine drive. The current solution is to collect similar high definition data using special purpose vehicles regularly to update the changes. However, this solution is costly, especially for large-scale mapping in multiple cities. Instead, we propose a low cost solution which aims to update the driveable area of HD maps using crowd-sourced images taken from a smartphone.
- Ming-Fang Chang, John W Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, and James Hays. Argoverse: 3D Tracking and Forecasting with Rich Maps. In Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
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