Ryan Wolcott Receives Best Student Paper Award at IROS 2014
The paper presents a new visual localization algorithm that allows an autonomous car to precisely know where it is at with sub-lane precision.
![Ryan Wolcott](https://eecsnews.engin.umich.edu/wp-content/uploads/sites/2/2014/09/Ryanwolcott-e1585245651456.jpg)
![Ryan Wolcott](https://eecsnews.engin.umich.edu/wp-content/uploads/sites/2/2014/09/Ryanwolcott-e1585245651456.jpg)
Ryan Wolcott, a doctoral student in CSE, received a Best Student Paper Award at the 2014 IEEE/RSJ International Conferences on Intelligent Robot Systems. The award is given to the most outstanding paper authored primarily by, and presented by, a student. It is entitled, Visual Localization within LIDAR Maps for Automated Urban Driving, and was coauthored by his advisor Prof. Ryan Eustice.
Since self-driving cars have become reality, Wolcott delves into one of the most significant roadblocks to autonomous vehicles, which is the prohibitive cost of sensor suites necessary for localization.
![System image](https://eecsnews.engin.umich.edu/wp-content/uploads/sites/2/2014/09/Systemimage-e1585245682472.jpg)
![System image](https://eecsnews.engin.umich.edu/wp-content/uploads/sites/2/2014/09/Systemimage-e1585245682472.jpg)
Wolcott stated, “The paper presents a new visual localization algorithm that allows a car to precisely know where it is at with sub-lane precision. The proposed technique enables autonomous driving without having to rely on GPS, which is fragile for a number of reasons (multi-path, low-accuracy, GPS-denied conditions). The new algorithm allows for matching a forward-looking dash-cam image to a pre-built 3-dimensional LIDAR map of the environment. By generating thousands of synthetic views of the scene (similar to video game-like renderings of the world), we used normalized mutual information to evaluate these registration candidates. We compare our work against the state-of-the-art in LIDAR-based localization, showing that we can achieve a similar order of magnitude error rate with a sensor that is several orders of magnitude cheaper.”
The image above shows an overview of the researchers’ proposed visual localization system. They sought to localize a monocular camera with a 3D prior map (augmented with surface reflectivities) constructed from 3D LIDAR scanners.
Click here for a video summary of the paper.