Implementation of an Update Scheme for Monocular Visual SLAM

Abstract

An autonomous mobile robot is an intelligent agent which explores an unknown environment with minimal human intervention. Building a relative map which describes the spatial model of the environment is essential for exploration by such a robot. Recent advances for robot navigation motivate mapping algorithms to evolve into simultaneous localization and map-building (SLAM). Initial uncertainty is one of the key factors in SLAM. An update scheme of the feature initialization in monocular vision based SLAM will be briefly introduced, which is within a detailed implementation of feature detection and matching, and 3-D reconstruction by multiple view geometry (MVG) within extended Kalman filter (EKF) framework. Experiments clearly show that the proposed scheme can maximize the optimization capacity of EKF.