Objective

2

In the robot's path planning and obstacle avoidance process

measuring the relative distance between the target and the sensor is very important. Distance measurement methods based on optical sensors have the advantages of portability

intuitiveness

and low cost. Therefore

they are widely used in real applications. Visual distance measurement methods commonly include monocular visual measurement and binocular visual measurement. There are two main methods for binocular image measurement: estimating the distance between the target and the camera by comparing two blurred images with different optical parameters or measuring the distance by comparing the parallax maps of these two images. However

both methods require sufficient baseline distance between the left and right cameras. Therefore

they are not suitable for smaller installation sites (unmanned boats). Furthermore

they are computationally expensive and difficult to be used in real-time applications. Monocular vision measurement was first proposed by Pentland. The basic principle is calculating the distance between the edge in the target image and the camera through the brightness changes on both sides of the target edge. Compared with binocular vision measurement

monocular vision measurement requires only one camera

and its calculation principle is simple. However

the following problems still exist in practical applications. 1) These methods are based on the exact position evaluation of the target step edge in a single image. However

this condition is difficult to be satisfied in practical applications. Therefore

a preprocessing module for detecting edges should be added

which increases the complexity and reduces the adaptability of these methods. 2) When using the Gaussian filter to approximate the fuzzy point spread function

the problems of losing the edge of slow change and the low accuracy of edge positioning in Gaussian filtering are ignored. The positioning accuracy directly affects the accuracy of monocular measurement. 3) The blurring degree of an image is related directly to the accuracy of measurement. In spite of this

the distance measurement is performed by the method to re-blur the target image multiple times. Moreover

the quantitative relationship between the number of re-blur process and the accuracy of the distance measurement is unknown. Therefore

the distance measurement accuracy is difficult to control and improve from the perspective of active blurring.

Method

2

This study proposes an adaptive method of measuring distance on the basis of a single blurred image and the B-spline wavelet function. First

the Laplacian operator is introduced to quantitatively evaluate the blurring degree of the original image and locate the target edge automatically. According to the principle of blurring

the blurring degree of the original image increases as the number of re-blur increases. When the blurring degree reaches a certain amount

it will change more slowly until it cannot judge the change in the blurring degree caused by two adjacent re-blurs. On the other hand

as the re-blur number increases

the distance measurement accuracy first increases and then decreases rapidly when the re-blur number reaches a certain value. Therefore

the relationship among the calculated blurring degree

re-blur number

and distance measurement error is established. Then

the B-spline wavelet function is used to replace the traditional Gaussian filter to re-blur the target image actively

and the optimal re-blur number is adaptively calculated for different scene images on the basis of the relationship among the blurring degree of image

number of blurs

and measurement error. Finally

according to the change ratio of the blurring degree on both sides of the step edge in the original blurred image

the distance between the edge and the camera is calculated.

Result

2

A comparison experiment with respect to different practical images is conducted. The experimental results show that the method in this study has higher accuracy than the method of measuring distance on the basis of re-blur with the Gaussian function. Comparatively

the average relative error decreases by 5%. Furthermore

when the distance measurement is performed on the same image with different re-blur numbers

the measurement accuracy is higher with the optimal re-blur number obtained with the relationship among the blurring degree of image

number of blurs

and measurement error.

Conclusion

2

Our proposed distance measurement method combines the advantages of traditional distance measurement method with the Gaussian function and the cubic B-spline function. Owing to the advantages of B-spline in progressive optimization and the optimal re-blur number achievement method

our method has higher measurement accuracy

adaptivity

and robustness.