In collaboration with Ford Motor Company, QUT robotics researchers have developed a method for instructing an autonomous vehicle’s cameras which to use for navigation. Senior author and Australian Research Council Laureate Fellow Professor Michael Milford said the study is the result of an investigation into how cameras and LIDAR sensors, which are frequently used in autonomous vehicles, can better perceive their surroundings.
According to Professor Milford, the fundamental concept is to determine which cameras to utilize at various sites throughout the world based on prior experience there.
For instance, the system may decide to employ a specific camera on consecutive trips to a portion of road after discovering that it is highly helpful for tracking the position of the car on that particular stretch of road.
The project is being led by Dr. Punarjay (Jay) Chakravarty on behalf of the Ford Autonomous Vehicle Future Tech group.
According to Dr. Chakravarty, autonomous cars rely largely on being able to determine their location in the world by employing a variety of sensors, including cameras.
Knowing your location enables you to make use of map data that is also helpful for finding other dynamic items in the scene. People may cross at a certain intersection in a specific manner.
Accurate localization is essential for object detection because this may be used as prior knowledge. This research also enables us to concentrate on the optimal camera at any given time.
The team has also had to come up with fresh methods for assessing an autonomous vehicle positioning system’s performance in order to make headway on the issue.
Dr. Stephen Hausler, a co-lead researcher, stated: “We’re not just interested in how the system functions when things are going well; we’re also interested in what happens in the worst-case scenario.”
This study was conducted as a component of a broader, more fundamental Ford research effort that examined how cameras and LIDAR sensors, which are frequently used in autonomous vehicles, might better comprehend their surroundings.
In addition to being presented at the next IEEE/RSJ International Conference on Intelligent Robots and Systems in Kyoto, Japan in October, this work was recently published in the journal IEEE Robotics and Automation Letters .
Ford researchers Punarjay Chakravarty, Shubham Shrivastava, and Ankit Vora worked with QUT professors Stephen Hausler, Ming Xu, Sourav Garg, and Michael Milford.
By permission of Queensland University of Technology (QUT) .
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