Animals, such as Savannah sparrows and North American monarch butterflies, are able to obtain compass information from skylight polarization patterns to help them navigate effectively and robustly. Inspired by excellent navigation ability of animals, this paper proposes a novel image-based polarized light compass, which has the advantages of having a small size and being light weight. Firstly, the polarized light compass, which is composed of a Charge Coupled Device (CCD) camera, a pixelated polarizer array and a wide-angle lens, is introduced. Secondly, the measurement method of a skylight polarization pattern and the orientation method based on a single scattering Rayleigh model are presented. Thirdly, the error model of the sensor, mainly including the response error of CCD pixels and the installation error of the pixelated polarizer, is established. A calibration method based on iterative least squares estimation is proposed. In the outdoor environment, the skylight polarization pattern can be measured in real time by our sensor. The orientation accuracy of the sensor increases with the decrease of the solar elevation angle, and the standard deviation of orientation error is $0.{15}^{\circ }$ at sunset. Results of outdoor experiments show that the proposed polarization navigation sensor can be used for outdoor autonomous navigation.

The nature animals have evolved highly efficient and robust organs that support their complex daily navigation tasks. To mimic animal’s navigation capability, we present a novel bio-inspired navigation system that draws inspirations from nature animals in this paper. The system consists of a three-axis magnetometer, a monocular camera, a micro inertial measurement unit (MIMU) and a polarization camera. While dead reckoning, orientation, and landmark recognition are considered as three most important capability for various species, we also designed corresponding algorithms based on the bio-inspired sensing system to perform autonomous navigation. In detail, the dead reckoning component is accomplished by integrating the monocular camera and the MIMU into a visual inertial odometry (VIO) and the orientation capability is achieved by combining the absolute orientation from the magnetometer with the relative orientation from the VIO. A loop closure detection is then used as the landmark recognition component to reduce the navigation drifts. All the three components are fused with a graph optimization method to generate the robust navigation result. To valid the proposed navigation sensing system and the algorithms, we have conducted series of experiments on ground and aerial unmanned vehicles, and have added orientation noise to testify the accuracy and robustness of the system.