Simultaneous Localization and Mapping with Stereo Vision

— In the simultaneous localization and mapping (SLAM) problem, a mobile robot must build a map of its environment while simultaneously determining its location within that map. We propose a new algorithm, for visual SLAM (VSLAM), in which the robot’s only sensory information is video imagery. Our approach combines stereo vision with a popular sequential Monte Carlo (SMC) algorithm, the Rao-Blackwellised particle filter, to simultaneously explore multiple hypotheses about the robot’s six degree-of-freedom trajectory through space and maintain a distinct stochastic map for each of those candidate trajectories. We demonstrate the algorithm’s effectiveness in mapping a large outdoor virtual reality environment in the presence of odometry error. Keywords—Localization, mapping, stereo vision, RaoBlackwellised particle filter, visual landmarks

• Type: Conference
• Year: 2006
• Where: ICARCV
• Authors: Matthew N. Dailey, Manukid Parnichkun
• Keywords: Combines Stereo Vision, ICARCV 2006, Particle filter, Stereo Vision