A Flexible and Efficient Loop Closure Detection Based on Motion Knowledge

Abstract

Loop closure detection (LCD) is an essential module for simultaneous localization and mapping (SLAM), which can correct accumulated errors after long-term explorations. The widely used bag-of-words (BoW) model can not satisfy well the requirements of both low time consumption and high accuracy for a mobile platform. In this paper, we propose a novel LCD algorithm based on motion knowledge. We give a flexible and efficient detection strategy and also give flexible and efficient combinations of a global binary feature extracted by convolutional neural network (CNN) and a hand-crafted local binary feature. We take a continuous motion model, grid-based motion statistics (GMS) and motion states as motion knowledge. Furthermore, we fuse the proposed LCD with a visual-inertial odometry (VIO) system to correct localization errors by a pose graph optimization. Comparative experiments with state-of-the-art LCD algorithms on typical datasets have been carried out, and the results demonstrate that our proposed method achieves quite high recall rates and quite high speed at 100% precision. Moreover, experimental results from VIO further validate the effectiveness of the proposed method.