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Open Access Motion Estimation Using Visual Odometry and Deep Learning Localization

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Modern day vehicles and especially driver assisted cars rely heavily on advanced sensors for navigation, localization and obstacle detection. Two of the most important sensors are the Inertial Measurement Unit and the Global Positioning System devices. The former is subject to wheel slippage and rough terrain, while the latter can be noisy and dependent on good satellite signals. The addition of camera sensors enables the usage of visual data for navigation tasks such as lane tracking and obstacle avoidance, localization tasks such as motion and pose estimation, and for general mapping and path planning. The proposed approach in this paper allows camera systems to work in conjunction with or replace both Inertial Measurement Unit and the Global Positioning System sensors. The proposed visual odometry and deep learning localization algorithms improve navigation and localization capabilities over current state-of-the-art methods. These algorithms can be used directly in today's advanced driver assistance systems, and take us one step closer towards full autonomy.
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Keywords: DEEP LEARNING; LOCALIZATION; SELF-DRIVING CARS; STEREO VISION; VISUAL ODOMETRY

Document Type: Research Article

Publication date: January 29, 2017

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

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