Data association and map management for robot SLAM using local invariant features

Abstract

To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scale-variant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to detect scale- and orientation-invariant features as well as provide a robust representation of visual landmarks for SLAM. The procedures of detection, description and matching of regular SURF algorithms are modified in this paper in order to provide a robust data-association of visual landmarks in SLAM. Furthermore, the effective method of map management for SURF features in SLAM is also designed to improve the accuracy of robot state estimation.