Geometric Map-Assisted Localization for Mobile Robots Based on Uniform-Gaussian Distribution

摘要

Drift and scale ambiguity are two main issues which reduce localization accuracy in monocular visual odometry (MVO). It is necessary to propose a unified model to represent these measurement uncertainties. In this paper, we present a geometric map-assisted localization approach for mobile robots equipped with MVO. We model the measurement of MVO as a group of particles, which obey uniform-Gaussian distribution and cover measurement uncertainties including scale ambiguity and measurement randomness. The saliency of each particle can be obtained from the distribution to indicate raw measurement certainty of MVO. Geometric map-assisted shape matching is implemented as the measurement model to assign consistency to the particles generated from the distribution. Both saliency and consistency are considered in particle weights determination. Furthermore, based on the statistical properties of the probability distribution, a parameter estimation scheme is proposed to narrow down the scale ambiguity of MVO while resampling particles. Experiments with KITTI dataset have demonstrated that the proposed approach greatly enhances positioning accuracy, with average localization error of 6.54 m in over 15.89 km run.