ORB_SLAM2

us, memorizing is AIs strong point. 2. You need to have mechanics that are more forgiving. If I accidentally position my hand the wrong way, I might accidentally squeeze someone a bit, but I won't crush their bones, because the mechanics of my arm are flexible. We'll need better accentuators and elastic casings. And just for the sake of discussion, here's my replies for each problem category. Simultaneous Location and Mapping: There are libraries that work well enough for your robot to localize itself in a building-sized environment with just a single camera. I'd consider this solved. https://github.com/raulmur/ORB_SLAM2 As for the obstacles, also for humans it is mostly guessing if you want to step on that blanket or if there'll be something fragile or slippery inside. Lost Robot Problem: Most SLAM solutions are good enough that you could just regenerate the map from scratch every time that there was a gap in your perception. ORB-SLAM2 also has a loop and merge detection module so that if you reset its tracking and then it walks into a known environment, it can merge the old data into its new state. Depth estimation: It works well enough in practice. https://www.stereolabs.com/zed-2/ Scene understanding: I don't know about you, but when I drive the highway, I sometimes have dead flies on my windshield. Apparently, they aren't that clever after all. Position estimation: It works exceptionally well for VR markers. In general, those solutions tend to be called "Visual Odometry" https://www.youtube.com/watch?v=fh5dLF3dmr0 Affordance discovery: This is mostly a memorization problem, so a perfect candidate for AI.

r to the semantic map being generated by the camera and radar. Your example of brake lights is good, as it provides a cue as to what is likely to happen. It also means that the high latency of a visual processing system is less of a problem, as the model can be updated by the lidar. Camera picks up a car, the model attaches it to the pointcloud it thinks is the car from the lidar/radar. When the lidar/radar updates it can create a prediction of where the visual system should look. > detecting perpendicular cars vs parked cars at that range? You don't have to. lidar gives you a point cloud, those points can be roughly translated into hard surfaces. if a hard surface is in your path or predicted path, take action until it isn't. Dealing with pointclouds for object avoidance is much much more computationally simpler, than having to infer 3d from either structure from motion, stereo or both. https://github.com/raulmur/ORB_SLAM2 if you look at this slam system, the pointcloud it generates is very sparse compared to a lidar: https://youtu.be/W3ELziPYn5k?t=13 But as I said before, you need other sensors to get other data/corroborate world model. >lidar was quite sensitive to dust, rain, snow, and fog. It is indeed, like any other sensor > Will weather cause the lidar to decay in similar ways to human eyes? Most lidars operate in the far infrared. so will handle decay differently. Depending on frequency it'll either be sensitive to moisture, or not at all.

e-posenet). You may also want to watch out for this conference http://cvpr2017.thecvf.com/ scheduled for the end of this month and is usually a good source of cutting edge research...

ENTS Strong math background. Ability to write concise, reliable, and readable code quickly. Github with stars is nice. Computer vision experience a plus. Basically someone who could have written https://github.com/mapillary/OpenSfM or https://github.com/raulmur/ORB_SLAM2" http://www.comma.ai/positions.html Hotz has been claiming that they would not need mapping and SLAM like Google's self-driving car. The whole "Looking for someone to build a SLAM algorithm capable of scaling to the world" sure sounds like "I guess this time he couldn't find anyone on IRC to finish the hard parts for him and let him take the credit" that lawnchair_larry posted elsewhere in this discussion (https://news.ycombinator.com/item?id=12818219).