Building topological map using omnidirectional image matching

This paper addresses the problem of constructing a globally consistent topological map by a vision-based mobile robot in an indoor environment. The vertices of the map represent the appearance of certain places and the edges represent the relative position between two vertices. An omnidirectional image matching algorithm is proposed to solve two problems: detecting loop closure and calibrating odometry feedback, which uses matched radial lines in successive frames to estimate the rotational angles of the robot. The map construction task consists of two steps: when no loop closure is detected, the robot use image matching results to calibrate the odometry data. After loop closure is detected, an energy-based spring model is used to refine the structure of the global map by minimize the energy of the model. Experimental evidence of the correctness of this method is given in unmodified indoor environments.