The cost of LIDAR is coming down
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Where LIDAR typically falls down is in adverse conditions (rain, snow, low sunlight in the horizon). There's a group at my university, MDAR, who is prototyping a tech to better overcome the adverse weather issues through a better laser sensing technique.
Here's an article with a little more technical information [1].
Disclaimer: I've done some part time work on market research for MDAR.
For military applications it's more useful to have one radar at high range/power than to have n radars at low range/power, while it's just the opposite for civilian lidar applications. So I don't see military radar SOP as evidence that this is inherently difficult.
With lidars you won't be able to adjust frequency because that's not how lasers work. Well, you can with free electron lasers but nobody is going to be putting those in lidars. But a laser is directional in a way that a laser isn't. You will occasionally get some false returns but real objects persist from scan to scan in a way that noise doesn't. I've worked with a small fleet of lidar equipped robots and lidar cross talk never caused us any problems. Sunlight on the other hand could be a big problem but automotive lidars are all rated for the outdoors.
This seems to be a common complaint about new technologies; people claim that somehow anti-social, violent or abnormal behaviour will be a problem merely because it is _possible_.
This is true of any pointcloud system though. No system returns density though, so you still have to make assumptions about what is worth running into at what resolution. So if a leaf falls in front of your car, you don't immediately slam on the brakes.
I don't know of any vision systems that do object recognition immediately, in fact I'm not even sure that is possible - or desirable. I guess if you baked segmentation into the firmware it would serve as a fast classifier, but AFAIK nobody is even talking about doing that as you would need a very well trained vision library and classifier to deploy to FPGA.
I make the distinction because we build fairly dense pointclouds with monocular RGB cameras and VSLAM. LIDAR isn't required anymore. LIDAR is faster with lower error tolerances today, but nowhere near as cheap or portable. I suspect monocular SLAM will overtake LIDAR in the next couple of years as it's getting faster and more accurate everyday.
As you mentioned we'll have to see where economics takes over as cameras are much cheaper, but less accurate.
I'm curious if your camera-based solution is for outdoor or automotive use. If it is meant for outdoor use, how is performance with oncoming headlights, a low-horizon sun, or in snow?
The snow thing is interesting actually, but in a different way than you might think. Because we do loop closure it makes matching scenes that are temporally separated (eg. no snow vs snowy or occluded by leaves etc...) very hard.
This is true, as long as you're willing to treat everything your LiDAR sees equally. The problem with this is that you're treating, say, a patch of grass (which you might as well drive over), a rock (which you'd rather drive around) and a pedestrian (which you might rather stop for, as a moving obstacle) the same way. Object recognition is still a good idea with LiDAR. And I'm not even getting into noisy returns or "negative" obstacles (i.e. holes).
Disclaimer: has done research in object recognition with LiDAR ;)
Some LIDAR units give you "first and last", that is, the time to the first reflection and to the last one. For a solid surface, both values will be very similar. For snow, rain, and foliage, they'll differ quite a bit. This is used in aerial surveys, where "first" gives you the tops of trees and "last" gives you the ground. It hasn't usually been available for ground-based LIDAR units, but there's no reason it couldn't be.
My wife is an archaeologist and we've seen lots of discoveries of buried historical sites found by flying over areas with airplanes and expensive (and large) LIDAR systems. It would be great to be able to disrupt that with prosumer equipment.
http://news.mit.edu/2015/algorithms-boost-3-d-imaging-resolu...
The same is true for object detection : http://www.cvlibs.net/datasets/kitti/eval_object.php
And tracking : http://www.cvlibs.net/datasets/kitti/eval_tracking.php
turning them into a point cloud isn't that difficult and much better than lidar for recognition purposes.
I am also thinking about a 3d camera. Maybe those are getting good enought to measure distance.
And, since the vehicle is moving (most of the time) anyway, maybe multiple returns can be integrated something like a synthetic aperture radar system to form a more complete picture.
Based on the ToF principle, the cameras employ an integrated light source. The emitted light is reflected by objects in the scene and travels back to the camera, where the precise time of arrival is measured independently by each pixel of the image sensor, producing a per-pixel distance measurement.
Given these kinds of liability concerns, it might be that reducing the cost of the tech won't reduce the cost of the product much.
There are quite good eye-safe flash LIDAR units.[1] Advanced Scientific Concepts in Santa Barbara makes some.[1] A pulsed laser sends out a flash, and the receiver has a 128x128 pixel chip with time of flight counters for each pixel. Range in hundreds of meters, in sunlight. No moving parts except a fan. Cost is about $100K, because they are hand-made using custom ICs produced in tiny volumes. Space-X uses one on the Dragon spacecraft to dock with the space station.
Back in 2003, when we were preparing for the DARPA Grand Challenge, I went down to Santa Monica to see the thing. But the demo was of components on an optical bench, aimed out a garage door into a parking lot. The technology wasn't ready to use yet, but it was clearly the right idea, something that would become cheap in volume. They were working more on making the device more sensitive than on making it cheaper, because they were getting DoD funding. Some of their units even have a photomultiplier tube stage, like an image intensifier. The photoelectric effect is at the atomic level, and so fast you can use it in front of a time of flight device.
So we ended up using the SICK LMS scanner, like everybody else.
There's a tradeoff between field of view and range with flash LIDAR, because there's a limited amount of illumination power. A single one of these devices won't give a full circle of scan, like the Velodyne rotating devices.
There's a cheaper, but less precise, approach, used in the Kinect 360. That's the ZCam technology. See p. 13 of this paper [2] for how it works. It's a phase-shift type LIDAR; the outgoing beam is modulated with an RF signal, and the phase shift of the RF carrier is measured on return. That's also all solid state, but not as good at rejecting interfering illumination. It doesn't have digital counters for each pixel; the timing system is analog. So they don't need counters running at tens of GHz behind every pixel.
Once someone is ready to order > 100,000 flash LIDARs, they'll get cheap. The technology is not inherently that expensive.
The main reason for having a LIDAR is not to see obstacles. It's to profile the terrain ahead. You can detect potholes and drop-offs. Since we were driving off-road for the DARPA Grand Challenge, that was absolutely essential. Automatic driving systems for on-road use can sleaze by without doing that. Most of the time. Until they encounter a big pothole, or are misled by road markings into a ditch. That's why Google has that LIDAR up on top.
[1] http://www.advancedscientificconcepts.com/ [2] http://tesi.cab.unipd.it/47172/1/Tesi_1057035.pdf
Their stuff is much smaller and more advanced now and they're pushing the OEM angle pretty hard[2].
[1] http://www.advancedscientificconcepts.com/products/older-pro...
[2] http://www.advancedscientificconcepts.com/applications/autom...
My office is right next door to ASCar's. I won't say much because it's probably privileged information but let's just say things seem to be coming along quite nicely.
It looks like LIDAR products typically operate in the near-infrared, with two products I picked at random operating at 905nm[1] and 850nm[2]. They'd want to pick bands that are invisible to humans, ones where laser diodes exist that can be modulated, and ideally bands that suffer minimal attenuation from environmental factors like water. NIR meets those criteria pretty well.
1. http://velodynelidar.com/docs/datasheet/63-9229_VLP16_Datash...
2. https://sick-virginia.data.continum.net/media/dox/6/16/516/P...