Navigation of a Biomimetic Autonomous Underwater Vehicle by Using Stereo Fisheye Cameras in a Known Environment

This work describes a localization algorithm for a fish robot by utilization of stereo fisheye cameras, a compass, and an accelerometer in a known underwater environment. A theory of the stereo fisheye cameras calibration is introduced. Classifiers which use Haar-like features are trained by discrete AdaBoost algorithm and they are used to recognize known landmarks in the underwater environment. Relative distances between the fish robot and the landmarks are then estimated by using stereo features correspondence. Taking the relative distances with respect to the landmarks as observation information, an extended Kalman filter algorithm integrates them with heading angles and 3-axis accelerations from the compass and the accelerometer into the motion model. The extended Kalman filter localization algorithm generates position estimations for BAUV’s self-localization. Finally, the localization algorithm is verified by experimental data, and it can be demonstrated that localization accuracies are limited by the quantization errors of the cameras, the sensors noises, and the background complexity.