LiDAR News: Voyant Photonics, Aeye

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Techcrunch: NYC-based LiDAR startup Voyant Photonics raises $4.3M investment from Contour Venture Partners, LDV Capital and DARPA. The founding team of the startup came from Lipson Nanophotonics Group at Columbia University.

"In the past, attempts in chip-based photonics to send out a coherent laser-like beam from a surface of lightguides (elements used to steer light around or emit it) have been limited by a low field of view and power because the light tends to interfere with itself at close quarters.

Voyant’s version of these “optical phased arrays” sidesteps that problem by carefully altering the phase of the light traveling through the chip.

This is an enabling technology because it’s so small,” says Voyant CEO and co-founder Steven Miller. “We’re talking cubic centimeter volumes.

It’s a misconception that small lidars need to be low-performance. The silicon photonic architecture we use lets us build a very sensitive receiver on-chip that would be difficult to assemble in traditional optics. So we’re able to fit a high-performance lidar into that tiny package without any additional or exotic components. We think we can achieve specs comparable to lidars out there, but just make them that much smaller.

BusinessWire: Aeye publishes a whitepaper "AEye Redefines the Three “R’s” of LiDAR – Rate, Resolution, and Range." Basically, it proposes to bend the performance metrics in such a way that Aeye LiDAR looks better:

Extended Metric #1: From Frame Rate to Object Revisit Rate

It is universally accepted that a single interrogation point, or shot, does not deliver enough confidence to verify a hazard. Therefore, passive LiDAR systems need multiple interrogations/detects on the same object or position over multiple frames to validate an object. New, intelligent LiDAR systems, such as AEye’s iDAR™, can revisit an object within the same frame. These agile systems can accelerate the revisit rate by allowing for intelligent shot scheduling within a frame, with the ability to interrogate an object or position multiple times within a conventional frame.

In addition, existing LiDAR systems are limited by the physics of fixed laser pulse energy, fixed dwell time, and fixed scan patterns. Next generation systems such as iDAR, are software definable by perception, path and motion planning modules so that they can dynamically adjust their data collection approach to best fit their needs. Therefore, Object Revisit Rate, or the time between two shots at the same point or set of points, is a more important and relevant metric than Frame Rate alone.

Extended Metric #2: From Angular Resolution to Instantaneous (Angular) Resolution

The assumption behind the use of resolution as a conventional LiDAR metric is that the entire Field of View will be scanned with a constant pattern and uniform power. However, AEye’s iDAR technology, based on advanced robotic vision paradigms like those utilized in missile defense systems, was developed to break this assumption. Agile LiDAR systems enable a dynamic change in both temporal and spatial sampling density within a region of interest, creating instantaneous resolution. These regions of interest can be fixed at design time, triggered by specific conditions, or dynamically generated at run-time.

“Laser power is a valuable commodity. LiDAR systems need to be able to focus their defined laser power on objects that matter,” said Allan Steinhardt, Chief Scientist at AEye. “Therefore, it is beneficial to measure how much more resolution can be applied on demand to key objects in addition to merely measuring static angular resolution over a fixed pattern. If you are not intelligently scanning, you are either over sampling, or under sampling the majority of a scene, wasting precious power with no gain in information value.”

Extended Metric #3: From Detection Range to Classification Range

The traditional metric of detection range may work for simple applications, but for autonomy the more critical performance measurement is classification range. While it has been generally assumed that LiDAR manufacturers need not know or care about how the domain controller classifies or how long it takes, this can ultimately add latency and leave the vehicle vulnerable to dangerous situations. The more a sensor can provide classification attributes, the faster the perception system can confirm and classify. Measuring classification range, in addition to detection range, will provide better assessment of an automotive LiDAR’s capabilities, since it eliminates the unknowns in the perception stack, pinpointing salient information faster.

Unlike first generation LiDAR sensors, AEye’s iDAR is an integrated, responsive perception system that mimics the way the human visual cortex focuses on and evaluates potential driving hazards. Using a distributed architecture and edge processing, iDAR dynamically tracks objects of interest, while always critically assessing general surroundings. Its software-configurable hardware enables vehicle control system software to selectively customize data collection in real-time, while edge processing reduces control loop latency. By combining software-definability, artificial intelligence, and feedback loops, with smart, agile sensors, iDAR is able to capture more intelligent information with less data, faster, for optimal performance and safety.

Medium: Researches from Baidu Research, the University of Michigan, and the University of Illinois at Urbana-Champaign demo a way to hide objects from discovering by LiDAR:

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