Abstract
In order to locate the mobile robots in three-dimensional indoor
environment, mostly global navigation satellite system-denied space, a
monocular visual space positioning algorithm based on deep neural
network is proposed. First, we employ the lightweight YOLOv5 algorithm
for target detection, and the LibTorch deep learning framework is used
for model deployment to improve the inference speed. Moreover, a
multi-layer perceptron (MLP) neural network with four inputs and two
outputs is constructed, which regress the coordinates of the robot in
the field coordinate system to complete the target localization, and
this method is compared with the mathematical model solving algorithm to
reflect the accuracy and superiority of positioning algorithm based on
deep neural network. The proposed positioning and tracking system has
been successfully applied to ICRA robot competition, and results show
that the positioning error estimated by our method is within 10cm whilst
having good real-time performance.