A Methodology for Principled Approximation in Visual SLAM

Abstract

This paper proposes a methodology for exploiting approximate computing to reduce the time and energy requirements of Simultaneous Localization and Mapping (SLAM) algorithms, which are used in important problem domains like robotics and autonomous driving in which autonomous agents navigate through unknown environments. Algorithms for SLAM use sensors to probe the environment, integrate this information into a map of the surroundings (mapping), and determine where the agent is in this map (localization). Visual SLAM algorithms use cameras as sensors. They can be used in places where GPS information is not available, %such as inside buildings, but they have high computational requirements, leading to poor performance and high energy usage on embedded platforms.