Visual-Inertial Odometry on Chip: An Algorithm-and-Hardware Co-design Approach
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2017_rss_navion.pdf
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Author(s)
Zhang, Zhengdong
Suleiman, Amr AbdulZahir
Carlone, Luca
Sze, Vivienne
Karaman, Sertac
Date Issued
July 2017
Journal
Robotics: Science and Systems
Citation
Zhang, Zhengdong, Amr Suleiman, Luca Carlone, Vivienne Sze, Sertac Karaman. "Visual-Inertial Odometry on Chip: An Algorithm-and-Hardware Co-design Approach." Robotics: Science and System XIII, Cambridge, Massachusetts, 2017.
Version
Author's final manuscript
Abstract
Autonomous navigation of miniaturized robots (e.g., nano/pico aerial vehicles) is currently a grand challenge for robotics research, due to the need of processing a large amount of sensor data (e.g., camera frames) with limited on-board computational resources. In this paper we focus on the design of a visual-inertial odometry (VIO) system in which the robot estimates its ego-motion (and a landmark-based map) from on- board camera and IMU data. We argue that scaling down VIO to miniaturized platforms (without sacrificing performance) requires a paradigm shift in the design of perception algorithms, and we advocate a co-design approach in which algorithmic and hardware design choices are tightly coupled. Our contribution is four-fold. First, we discuss the VIO co-design problem, in which one tries to attain a desired resource-performance trade-off, by making suitable design choices (in terms of hardware, algorithms, implementation, and parameters). Second, we characterize the design space, by discussing how a relevant set of design choices affects the resource-performance trade-off in VIO. Third, we provide a systematic experiment-driven way to explore the design space, towards a design that meets the desired trade-off. Fourth, we demonstrate the result of the co-design process by providing a VIO implementation on specialized hardware and showing that such implementation has the same accuracy and speed of a desktop implementation, while requiring a fraction of the power.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Microsystems Technology Laboratories
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DOI of Published Version
http://rss2017.personalrobotics.ri.cmu.edu/program/papers/
