This article is intended to help users in determining the appropriate stereo vision system specification for their end applications.

This article is intended to help users in determining the appropriate stereo vision system specification for their end applications

Stereo vision has been a large research topic in computer vision for several years. Its many applications include robotics, virtual reality (VR) and augmented reality (AR), droned, 3D reconstruction, people counting, inspection, and volume and shape estimation. This article is intended to help the readers in selecting the proper stereo vision system for their end applications.

Application case study: Box volume measurement

For the purposes of these discussions, we will consider how to select the required parameters for a stereo vision system based on the end application. In this case, our end application is to measure the volume of a box. The parameters that can be altered to meet the end application’s requirements are as follows:

Image sensor resolution.

Baseline (the distance between the two image sensors).

Focal length of the lens.

Pixel size of the image sensor.

Image sensor resolution refers to the number of pixels present in the image sensor. Increasing the image sensor resolution increases the depth resolution of the stereo vision system. The baseline is the distance between the left and right cameras (image sensors). Increasing the baseline can increase the depth range. The focal length is the point where light rays converges to form a sharp image on the digital sensor. The pixel size is the size of an individual pixel in the image sensor.

Let’s assume that the default specification of our stereo vision system is as follows:

Focal length = 4.3mm

Baseline = 60mm

Resolution = 640 * 480Pixel

size = 6um

In the remainder of this column we will be discussing these different scenarios and the way in which they affect the values of the vision system’s parameters.

Scenario 1: Ideal conditions

In this case, let’s assume that the box is placed one meter from the stereo vision system, and the dimensions of the box are a 1-meter cube as illustrated below:

(Source: e-consystems.com)

The formula for depth calculation is as follows:

By applying our default specification to the above formula and by varying the difference in the pixel position value from 1 to 64, the minimum depth range can be plotted as illustrated in the following graph.

(Source: e-consystems.com)

The difference in pixel position value of 64 is an algorithmic parameter that can be altered based on the sensor resolution and the system speed. As the resolution of the image sensor increases, the difference in pixel position range value also needs to be increased due to the high difference in pixel position between the two images.

From the above graph, we can interpret that the depth range of our stereo vision system is 0.7 to 3 meters. Since our box is within the depth range of the stereo vision system, and the accuracy of the system is in the centimetre range, the volume of the box can be measured exactly, so no customization of this first-pass system is necessary since the system meets the end application requirements.

Scenario 2: Simultaneous measurement of two boxes, where one is out of range

In this scenario, two boxes are placed at different depths. Both boxes are 0.5-meter cubes, and the volumes of both boxes must be measured simultaneously.

(Source: e-consystems.com)

As we previously noted, for the purposes of this scenario we are assuming that our boxes are placed at different depths. The depth range associated with our default stereo vision specification is 0.7 to 3 meters, but let’s say that the second box is placed at 3.5 meters. In this case, our current implementation cannot be used to estimate the volume of the second box since it is out of depth range.

The solution is to increase the baseline to 120mm in order to increase the depth range. Thus, our altered camera specification is as follows:

Focal length = 4.3mm

Baseline = 120mm

Resolution = 640 *480

Pixel size = 6um

By applying our new parameters to our original formula, and by varying the difference in the pixel position value from 1 to 64, the minimum depth range can be plotted as illustrated in the following graph.

(Source: e-consystems.com)

From this graph, we can determine that the depth range of our system has increased from (0.7 to 3m) to (1.3 to 4m). Thus, by changing the baseline, both the boxes are now within the depth range of the system for accurate measurement.

Scenario 3: The box is too tall

In this scenario, we will assume that the top of the box is 1.5 meters from the camera, and the size of the box is 1 meter in length, 1 meter in breadth, and 2.5 meters in height.

(Source: e-consystems.com)

In the above scenario, the increased height of the box causes it to exceed the depth range that can be accommodated by the default specification, which means the entire box cannot be covered by the system. Once again, the solution is to increase the baseline to 120mm in order to increase the depth range. Thus, our altered camera specification is as follows:

Focal length = 4.3mm

Baseline = 120mm

Resolution = 640 *480

Pixel size = 6um

By applying our new parameters to our original formula, and by varying the difference in the pixel position value from 1 to 64, the minimum depth range can be plotted as illustrated in the following graph.

(Source: e-consystems.com)

From this graph, we can determine that the depth range of our system has increased from (0.7 to 3m) to (1.3 to 4m). Thus, by changing the baseline, the entire box can now be measured accurately.

Scenario 4: The box is too small

Now let’s consider what will happen if our boxes are located at a distance of 2 meters and the sizes of the boxes are 10cm long, 10cm wide, and 0.5cm tall.

(Source: e-consystems.com)

Since the boxes to be measured are less than a centimetre tall, the default specification of our system cannot be used for accurate measurement. The solution to measure small objects accurately is to increase the resolution of the sensors. Consider our altered camera specification as follows:

Focal length = 4.3mm

Baseline = 60mm

Image sensor resolution = Greater than VGA resolution (Maximum resolution possible)

Difference in pixel position range value = 128

Pixel size = 6um

By applying our new parameters to our original formula, and by varying the difference in the pixel position value from 1 to 128, the minimum depth range can be plotted as illustrated in the following graph.

(Source: e-consystems.com)

Increasing the resolution of the image sensor allows more detail to be captured by the system. Increasing the resolution and the difference in pixel position value increases the depth resolution of the stereo vision system, thereby allowing small volume objects to be measured accurately. The same result can also be achieved by reducing the pixel size.

Scenario 5: The box is very close to the camera

In this scenario, we will assume that the box to be measured is located 0.5 meters from the system. The box is an 0.5-meter cube. The depth range of our default system is around 0.7 to 3m.

(Source: e-consystems.com)

The solution in this case is to decrease the focal length, thereby decreasing the minimum depth range of the stereo vision system. Consider what happens when we reduce the focal length to 2.8mm to generate a new camera specification as follows:

Focal length = 2.8mm

Baseline = 60mm

Resolution = 640 * 480

Pixel size = 6um

By applying our new parameters to our original formula, and by varying the difference in the pixel position value from 1 to 64, the minimum depth range can be plotted as illustrated in the following graph.

(Source: e-consystems.com)

From this graph, we can determine that the minimum depth range of our system has been reduced 0.7 to 0.4m, which means that — even though it’s close to the system — that box can be measured accurately.

Additional applications

Below are some additional applications coupled with suggestions for altering the appropriate parameters from our default system specification.

(Source: e-consystems.com)

Conclusion

I hope that this article helps you when it comes to defining the appropriate specification for your stereo vision system depending on your target application. In the additional resources section below are a few links related to stereo vision systems.

Additional resources

About the Author

Prashanth Ragothaman is an engineer at e-con Systems working on camera systems and algorithms. Prashanth is excited to build prototypes for new use cases and ideas. He likes to “go deep” in understanding complex technologies and building systems from scratch.