A Daisy-Chaining Visual Servoing Approach with Applications in Tracking, Localization, and Mapping

Abstract

Recent advances in image processing, computational technology and control theory are enabling vision-based control, localization and mapping to become more prevalent in autonomous vehicle applications. Instead of relying solely on a global positioning system (GPS) or inertial measurement units (IMU) for navigation, image-based methods are a promising approach to provide autonomous vehicles with position and orientation (i.e., pose) information. Specifically, rather than obtain an inertial measurement of an autonomous vehicle, vision systems can be used to recast the navigation, localization, control and mapping problems in terms of the image space. Applications involving localization andmapping using camera as a sensor are often described as Visual Simultaneous Localization and Mapping (VSLAM) (Davison et al., 2007; Eustice et al., 2005; Goncalves et al., 2005; Jensfelt et al., 2006; Jung & Lacroix, 2003; Kim & Sukkarieh, 2003; Se et al., 2002), wherein the camera is the main sensor used to estimate the location of a robot in the world, as well as estimate and maintain estimates of surrounding terrain or features. There are many overlapping ways to categorize VSLAM approaches. Some authors (e.g., (Eustice et al., 2005; Jensfelt et al., 2006; Se et al., 2002)) make a distinction between “local VSLAM” and “global VSLAM”. Many VSLAM approaches use probabilistic filters (e.g., extended Kalman filter or particle filter) (Davison et al., 2007; Eustice et al., 2005; Jensfelt et al., 2006; Jung & Lacroix, 2003; Kim & Sukkarieh, 2003), typically estimating a state vector composed of the camera/robot position, orientation and velocity, and the 3D coordinates of visual features in the world frame. An option to a filtered based approach is the use of epipolar geometry (Goncalves et al., 2005; Se et al., 2002). A final possible category are methods that 2