**4.4 Camera underwater test**

Given a pattern (landmark) and a camera, then the minimum and maximum pattern recognition range can be predetermined. And this information will be used for vehicle's underwater path planning. Under this consideration, we carry out a series of test measuring the minimum and maximum recognition range both in the air and in the water. Test results are shown in Fig. 8 and 9, from which we can see that the maximum recognition range in the water is approximately half of the one in the air. For the safety consideration, we force the vehicle to keep from the basin wall at least 1.5m throughout the various basin tests.

Fig. 8. Test environments.

28 Autonomous Underwater Vehicles

Given the camera intrinsic matrix � and the homography � in the image, we can get the three-dimensional relationship between the pattern and the camera (robot) using following

> �� � ����ℎ� �� � ����ℎ�, �� ���� � ��, ��� � ����ℎ��

To evaluate the developed vision algorithm, we carry out a series of ground tests. Fig. 7 shows the test results. First, eight dots in the pattern are extracted from the image (Fig. 7-a). And Fig. 7-b shows the selected images for camera auto-calibration, and extracted camera

While evaluating the performance of proposed visual localization method, we mainly consider two points: one is the pattern recognition rate, and the other one is the accuracy of pose estimation. For pattern recognition rate, we arbitrarily move the camera and take 306 images. 150 of them are correctly recognized, 85 are failed to recognize, 61 are blurred because of camera movement, and 10 do not include the full pattern. Except 61 blurred images and 10 of missed-pattern images, the recognition rate is about 64%. However, consider the fact that about half of the non-recognized images are rotated more than 40

To evaluate the accuracy of pose estimation, we fix the camera and locate the pattern at 12 known positions between 400mm to 700mm distance. Calculated average pose estimation

Given a pattern (landmark) and a camera, then the minimum and maximum pattern recognition range can be predetermined. And this information will be used for vehicle's

(5)

equations

**4.3 Lab-based evaluation** 

pose is shown in Fig. 7-c,d.

Fig. 7. Process of pose estimation.

**4.4 Camera underwater test**

degrees from the pattern, the recognition rate is about 81%.

error is 0.155mm and the standard deviation is 1.798mm.
