#207 - Leaf Jiang - CEO, NODAR

“You can measure distance reliably using two cameras, called stereo vision. Based on the angles to the target using two images from two cameras, we can triangulate the position to everything in the scene. This is a conversion not of time-to-distance, but angles-to-distance.”

There has been a lot of talk recently about vision versus LiDARs and RADARs. I hosted Leaf Jiang, CEO of a company called NODAR to learn more about the advantages and limitations of each technology, and how NODAR's own technology overcomes them. Their name is a nice play on the fact that their product is not RADAR or LiDAR, but in fact, uses vision to achieve resolution and depth perception better than either of them.

Instead of relying on machine learning models to interpret the feed from the cameras, NODAR’s system, consisting of a pair of cameras, triangulates distance measures to points in the scene by measuring angles to the point from each of the cameras. There’s a lot of complicated geometry involved, which, sadly for the nerds amongst you, we will not go into.

All that said, NODAR’s colour-coded point clouds can be an incredibly powerful source of data for machine learning models that can then do everything from scene inference to path planning, possibly computationally more efficiently. 

I am sure you will love listening to my chat with Leaf on this episode of the AI in Automotive Podcast. With this episode, we wrap up season 02 of the AI in Automotive Podcast. If you have liked this season of the podcast, do share it with a friend or colleague, and give us a top rating wherever you get your podcasts.

Season 03 is coming on the 3rd of May, so keep an eye out.