A robust feature matching method for robot localization in a dynamic indoor environment

Abstract

In this paper, we report how the feature matching method can be applied to deal with the indoor mobile robot localization problem. We assume that a robot equipped with a laser rangefinder can scan the environment in real time and get the geometry features, and then the robot can match these features with those collected in advance to find the possible locations. This approach would face two difficulties. Since there are locations with similar features, the robot have to move around and do the scan and match several times to make sure the right location. There is another difficult problem, the features might not be fix in real-world dynamic environment, e.g. people might be walking through, furniture might be shifted; therefore, a robust feature matching method is needed for dynamic environment. This paper describes an efficient method using omni-directional feature grouping to improve the feature matching method for robot localization. With the laser rangefinder, a robot finds the 360 degree coverage information. Omni-directional feature grouping has the advantage of dividing all the features of a hypothetical position through different directions to generate multiple sets of environmental features. The method can reduce the affection of moving objects in a dynamic environment. Experimental results show that our method improve the accuracy rate and has low average errors.