A Flexible and Efficient Loop Closure Detection based on Motion Knowledge

Bingxi Liu, Yujie Fu, Fulin Tang, Yihong Wu

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-theart
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.