Online location recognition for drift-free trajectory estimation and efficient autonomous navigation

Abstract

Autonomous navigation is a desired capability for mobile robotic platforms, such as UAVs and UGVs. Efficient autonomous navigation, especially in unknown environments, requires accurate localization based on online location recognition. Traditional monocular camera-based location recognition methods can suffer from accuracy, speed and robustness issues. In this paper, a novel online location recognition (OLR) algorithm based on fast and efficient interestpoint detection and feature-based keypoint matching is presented. The OLR incrementally constructs a database of visited locations and robustly recognizes revisited locations, even in complex and cluttered scenes. The OLR capability is quantitatively evaluated using a mobile robot setup in a multi-room office building environment. We further present and validate two applications based on the proposed OLR algorithm. In the first application, the OLR algorithm is integrated with conventional SLAM and an efficient trajectory correction algorithm for drift-free trajectory estimation (DTE) and qualitatively evaluated in the same environment. In the second application, the OLR algorithm is integrated with an exploration strategy for efficient autonomous navigation (EAN) and evaluated using a UAV model in a simulated warehouse environment. Our experiments demonstrate the accuracy and efficiency of the proposed location recognition algorithm and its utility for drift-free trajectory estimation and efficient autonomous navigation.