Polarized skylight orientation determination artificial neural network
This work addresses orientation determination for navigation systems, likely in robotics or biomimetics, but appears incremental as it builds on existing neural network approaches with specific modifications.
The paper tackled orientation determination using polarized skylight by proposing an artificial neural network with dilated convolution and exponential function encoding, achieving improved accuracy as proven by experimental results on a public dataset.
This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect's encoding of polarization information, and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.