Lidar has a few advantages for object recognition.
- Longer range. At distances where a camera image will be just a bunch of indistinct pixels, lidar will be able to resolve a usable point cloud.
Only if you configure it to scan a particular spot at higher resolution, which then results in less frequent repaints of the overall scene. You can do something similar with a camera, too, provided you have a zoom lens and the ability to pan and tilt it. But in practice, if something is far enough away that it is just a bunch of indistinct pixels, it also probably doesn't matter yet.
- Temporal super-sampling. It's easier to combine point clouds from multiple samples to enhance object recognition. It's very difficult to do with cameras in a useful way.
Actually, I've seen a decent number of papers about multi-image superresolution. And one approach for getting depth information from a single camera is to use two consecutive shots while moving towards or away from the object in question, then using parallax differences to estimate its position. So there's a
lot you can do with information from cameras across multiple shots.
- No need for multiple front facing FOV cameras, the limitations of static optics do not apply.
Nobody, and I mean absolutely nobody is trying to do self-driving without multiple front-facing cameras. For one thing, it is not possible to determine the color of a traffic light with LIDAR, nor recognize turn signals to know when to slow down to let people in, nor read speed limit signs to know that the road is under construction and has a lower limit, etc. And if you only have one front-facing camera, then you have no redundancy, which means when it fails, your car becomes a death trap.
So no, LIDAR does not remove the need for multiple front-facing cameras.
- Removes the need for AI to do depth perception because it samples depth directly.
There are a number of approaches for computing depth from stereo images taken a fixed distance apart, and AFAIK, most of them involve no AI at all.
- Point clouds allow for estimation of which direction the object is facing in more easily than AI trying to do it. AI tends to work on flat image recognition, e.g. the back of a vehicle, but with the ability to handle transforms such as it being at an angle. To estimate direction it would need significantly more effort.
If I understand the comment correctly, you can do that on a single still image without stereo, with no AI at all, by using basic edge detection algorithms to estimate the average slope of the bottom edges of the bumper and the visible side of the car, then performing some basic math on that data. The hard part is actually recognizing that you're looking at a car to begin with, and thus need to know which way it is facing.
And, of course, if you care about whether it's the front or the back, that's still just basic image recognition. Are there headlights? If so, it is facing you.
But the more interesting data is knowing which way it is
moving. Cameras work just fine for that, too, obviously, because once you know the object's distance from parallax, you can compare how quickly the object is getting bigger compared with the ground at a similar distance and determine its relative velocity. It is just a lot more computationally complex to do it that way.
- Lidar works just as well in poor light, including low sun. As well as not requiring and indeed filtering ambient light, it is able to see things like a person wearing black against a black background because it can see they are separate from the background via depth measurement.
LIDAR may offer some advantages in low light. Then again, cars have headlights, and if you're driving too fast to make out a person in your headlights, then unless you're on the freeway where no pedestrians are allowed, you're driving too fast.