That's a big name. I'm suspicious it means "light intensity map" or something far less interesting than it sounds. Turn a light on or off, and then what? My Shark (ok Not a Roomba) manual says to leave the lights on. I suspect that's because its navigating by light intensity (light fixture bright spots).
I don't like to be dismissive, but your suspicion isn't well-founded and is very likely based on little or no background research on the topic. If you had, you'd have found that SLAM and VSLAM as a subset is a well-defined category of computer with a strong mathematical background in optical flow and pattern matching on images[0]. You'd have found a number of iRobot patents still in their enforceable time period directly referencing the use of cameras in those algorithms[1][2]. You'd have found marketing materials describing how those systems work that directly correlates with the technology described in those patents[3]. That's based on no special previous knowledge of those materials on my part, just the top few results from a couple minutes of DDG and Google Scholar searches.
Implying that is just "a big name" that means "something far less interesting than it sounds" is an uninformed opinion. Plus, even [0], which is 15 years old directly refutes your comments about the effect of lighting. Lighting is helpful because it illuminates the scene that a camera is seeing, in the same way that it's easier to go on a night hike on during a full moon. We don't reference our position off the moon, it just makes it easier to see where we're walking.
The processors in a Roomba (when I went to the plant a couple years ago) were all around $1. No room in a buck chip for that.
Ok I see it uses DSPs which is a budget version of image processing. Looks for moving marks on the walls to gauge distance. Kind of a cut-rate lidar. About what I figured.
According to the materials released by Qualcomm in 2018 (by which point, the Roomba i7 would have already have had lines in that plant), the processor used is a ARQ8009, a quad-core ARM processor commonly used in smartphones[0]. It's tough to find prices because the contracts are negotiated individually, but I'd put money on those chips costing much more than $1.00/piece.
Comparing VSLAM to lidar in any way besides saying that they technically can both be used to measure movement is also incorrect, and to actually call it a "kind of a cut-rate lidar" can only be due to a fundamental misunderstanding of VSLAM, lidar, or both.
Lidar measures distance along a single axis, with no additional information provided. You get movement by measuring changes in position by directly updating the distance value and inferring position change. It can be made robust by adding multiple lidar sensors, but is subject to weird edge cases with reflective materials or sunlight.
VSLAM only can measure change in vehicle position through the estimated position changes of significant image features through time, and provides no information about vehicle distance to the surroundings while the robot is standing still. You'd require stereo vision for that, and that does require more processing power, typically including a GPU like the one included in the NVIDIA Jetsons. You'd also typically use sensor fusion with an IMU, odometry, etc. to help with that.
Without stereo vision, you need motion to estimate position, which fundamentally makes VSLAM different from lidar, and makes your claim that it is "kind of a cut-rate lidar" completely false.
I'm not sure which DSP acronym you're referring to as a budget version of image processing , but if it has to do with digital signal processing, then yeah, of course. That's kind of the definition.
Jesus that's a world ground-speed record for pedantry right there.
Clearly, VSLAM is being touted as 'vision' when in fact its far less than that, less than LIDAR even, which was my point. Which I feel was completely understood, yet still a silly attempt to shame me was spewed out saying "But its less than LIDAR" which was true, what I wrote, and beside the point.
Oh, neat, looks like this debate has reached ad hominem attacks.
VSLAM is computer vision. Full stop. Here's an article about it in a Springer Computer Vision guide [0]. I don't know what your personal definition requires for that to be the case, but if it doesn't include point recognition and persistent tracking by an integrated RGB camera, then I think it's unreasonably out of line with that of the rest of the field.
Which, just so we're clear, is the eighth reputable primary source I've presented you with directly refuting claims you've made.
Yes, I understood your point that you feel VSLAM is less than lidar. My point is that it's like stating that the moon is purple. Comprehensible, but wrong. And not in a "beside the point" way, but in a way that directly laid out why a core piece of your argument regarding how you try to compare VSLAM with lidar is invalid. Pretty sure that's not pedantry.
Ok, when Roomba advertises 'computer vision' they're conjuring up images of recognizing rooms, finding furniture and pets, maybe building a model of your house. But what that DSP is delivering? Maybe a little feature-extraction, just so it can calculate relative motion/distance of a wall or obstacle. Calculated and forgotten.
In the scheme of computer vision sophistication, its right down there with a fly's eye. That was my point, and I believe it was well understood. The pedantry of insisting it is computer vision full stop. Well, yes, about 1% of what computer vision could be. Niggling over that point is what's annoying. Instead of a good-faith discussion of how primitive this is, how tiny a feature it is, how miniscule the benefit it brings the consumer. How its pasted on the Roomba feature list because, hey, cameras are cheap and we can sell that feature for maybe $100 because the consumer doesn't know.
Except it does do that. It's called persistent maps, and it's how the feature where you can say "Go vacuum the kitchen" works[0].
Also, I think you're underestimating both how valuable and complicated VSLAM is. Sure, it's not a multi-billion dollar state-of-the-art composite deep learning model trained on thousands of hours of data running on custom silicon, but it's hardly "This area is dark, and there's a bright thing over there!" Even if VSLAM only included visual odometry, that would still be a major improvement over other options in autonomous navigation, which is absolutely key in other domains that you seem to consider state-of-the-art. It's the same technology, and investments therein that make things like the RangerBot possible[1]. In a competitive market where BOM cost is a serious consideration, if you could get better results by not using VSLAM and relying only on something like laser odometry instead, that's what would be done.
Also, I'm honestly a little annoyed at your implication that I'm the one not having a good-faith discussion, while you have blatantly and repeatedly made false statements that could have been figured out by doing your own research, notable by your failure to provide any actual sources, instead making tangential anecdotal references to try to back up your statements. Heck, let's not forget your initial claim wasn't "limited vision" (which I debate anyway), but "no vision", and "no model of their environment" which again isn't simply true.
Roomba usually runs during the day when lights are off and all windows are darkened. If I open windows/turn on lights - there is no difference. Also you can draw on the map (that roomba makes) "no-go" area, and it will obey it.
