Integrated Polarized Skylight Sensor and MIMU With a Metric Map for Urban Ground Navigation

This paper presents a novel multi-sensor navigation system for the urban ground vehicle. The integration system can combine the measurements from a polarized skylight sensor, an inertial sensor, and a monocular camera. Utilizing the polarized skylight sensor, we propose a robust orientation algorithm with the total least squares to provide the orientation constraint for the integrating system. In our algorithm, the ambiguity problem of polarized orientation is solved without any other sensor. In order to enhance the algorithm’s robustness in the urban environment, we also propose a real-time method that uses the gradient of the degree of the polarization to remove the obstacles. With a monocular camera, we build a metric map and recognize places in the map to provide the position constraint for the integrating system. We develop the Kalman filter to integrate these constraints with the inertial navigation results to estimate the orientation and position for the ground vehicle. The results demonstrate that our proposed system outperforms other vision-based navigation algorithms–the RMSE of the position error is 2.04 m (0.01% of the travelled distance) and the RMSE of the orientation error is 0.84°. Finally, we present interesting insights gained with respect to the further work in sensors and robotics.