Changing disparity range dynamically

Hello! It’s been a while since the last post! That doesn’t mean we’ve been idle, though. There has been lots of progress on the stereo vision setup in terms of performance and robustness as we gear up for a demonstration in a week’s time. More on that in later posts though, right now I’d just like to talk about a small but important feature.

In a real car, when you see an object in the distance, you slow down as you approach the object, eventually coming to a halt close to it. We wanted to accomplish something similar to this for our autonomous car.

To achieve this, we need to be able to perceive the distance to said object. From the stereo pair of images and the disparity map, it is possible to calculate the absolute distance to an object, but that is computationally expensive, and we wanted to avoid this, and make the most of the necessary computations we have already performed.

As such, the solution we have implemented is thus:

  1. Let’s say the maximum number of disparities is set to 48 (16 * 3). With our camera setup, the closest object we can detect (i.e. brightest/whitest on the disparity map) is roughly 1 meter away.
  2. Let’s say we are 2 meters away from this object. As we get closer to it, the level of white corresponding to this object increases. Once this passes a certain threshold, we know we are close to the object, and can tell the car to slow down a notch.
  3. As the car slows down, we increase the maximum number of disparities to 16 * 4. Now the distance to the closest object we can detect decreases, so we don’t lose sight of it. We can then repeat the same procedure, where passing over a certain threshold level of white in our disparity map causes us to slow down, and increase the number of disparities.
  4. We repeat this process until we get to a maximum maximum number of disparities. Let’s say this is 128 (16 * 8), which gives a range of about 15 – 20 cm. Now when we cross over the whiteness threshold, we are travelling very slowly, and we know that we are about 15 – 20 cm away from an object, so we can tell the car to stop.

The actual numbers can be set depending on the required range, how close you want to stop to the object, and basically just experimenting around with the actual car.

There are some obvious limitations to this approach though:

  1. As we get closer to the object and increase the max number of disparities, the frame rate drops as calculating the disparity map takes longer. However, this coincides with the car slowing down, so we have more time per frame.
  2. Unless you have been ‘tracking’ an object as it gets closer, any object closer than what the initial number of disparities allows will not be detected. For example, if I stick my leg out about 20 cm in front of the car, it will not be detected and the car will crash into me. The disparity map will mostly be black (i.e. error pixels). To overcome this problem, we could perhaps modify the algorithm so that it increases the max number of disparities (thus allowing us to ‘look’ closer) if the number of error pixels is over a threshold, or simply stop the car if that is the case. We could also use a hardware proximity sensor as an input to put on an emergency break.

We have yet to see how this approach works with a relatively fast moving car, but testing it with moving objects in front of a stationary camera indicates promising results.

Hassan

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