At Rise of AI 2019 in Berlin, Tobias Bahnemann, co-founder and managing director of Toposens, presents a 3D ultrasound sensor that uses sound and AI to give robots and vehicles low-cost close-range environment perception. Comparing the idea to Batman's citywide sonar in The Dark Knight, he explains that Toposens sends out high-frequency sound, captures reflections with three microphones, and processes them on a small chip, the SoundVision 1, into a 3D point cloud, without needing light and while generating only 3 to 5 megabytes of data per hour, small enough for on-device AI processing. He argues the technology fills a gap left by camera and lidar systems, which struggle at close range, in poor lighting, with glass, or at prices that keep them out of many products, citing failures such as unreliable autonomous parking and undetected curbs. He describes four prerequisites for training reliable neural nets on this data (reliable, precise, dense and large datasets) and Toposens' staged roadmap from detecting static objects to dynamic ones and eventually predictive understanding, alongside partnerships with Nvidia and Infineon, named customers, and an ongoing funding round.
Key takeaways
- Toposens' 3D ultrasound sensor works independent of lighting conditions and costs less than comparable camera or lidar systems.
- The sensor produces only 3 to 5 megabytes of data per hour, small enough to run AI processing on the edge device.
- Bahnemann identifies four prerequisites for training a neural net on this sensor data: reliability, precision, density and large datasets.
- Toposens positions its five-meter-range sensor as complementary to lidar and cameras, not a replacement, especially for close-range maneuvering.
- The company had raised about two million dollars from Silicon Valley and German investors and was starting a new funding round at the time of the talk.
Detailed notes
A bat-inspired, low-cost 3D sensor
Bahnemann introduces Toposens by comparing its idea to Batman's fictional citywide sonar system in The Dark Knight, calling the company's device the real-world equivalent: a sensor that perceives its surroundings through sound the way bats do. The company's Initial Sense product, about to launch commercially within two months of the talk, sends out high-frequency sound, receives the reflected signals, and processes them into a three-dimensional point cloud. He highlights that the system works regardless of lighting conditions, is the cheapest 3D sensing technology on the market because it needs only a small processing chip, is optimized for short range up to five meters, and generates only 3 to 5 megabytes of data per hour of operation, small enough that AI processing can run on the edge device without uploading data.
Why close-range perception matters
He argues that autonomous systems in robotics, passenger cars and commercial vehicles have so far relied mainly on optical 3D cameras and lidar, both of which struggle at short distances or are too expensive to equip broadly, contributing to deaths, injuries and physical damage. As examples, he cites luxury cars whose autonomous parking functions work only about half the time due to poor sensor data, and demonstrates the sensor mapping a sofa in a home environment for vacuum-cleaner-robot applications, noting that unlike optical systems it detects glass well.
How the sensor works
Bahnemann explains that on the hardware side the system needs only a transducer, three microphones and the company's SoundVision 1 chip, plus a circuit board and housing; on the software side, the chip calculates the time of flight of each sound reflection and runs 3D processing to build the point cloud. He describes the current processing as lower-level AI focused on echo detection and precise timing analysis, plus a new cooperation with Infineon on transducer components and an existing partnership with Nvidia on neural nets for autonomous driving.
From reliable data to real intelligence
He lists prerequisites the team identified over the past one to two years for training a proper neural net on this data: reliable data with low discrepancy between sensors down to millimeter precision, high point density, and, once those are met, large datasets. He places Toposens between the first two of four development stages: after establishing reliable sensor data and pattern detection, the next step is recognizing static objects, followed by recognizing moving, dynamic objects, and finally what he calls real intelligence, where the system predicts what a detected person or object is about to do.
Roadmap, customers and funding
Bahnemann says Toposens is releasing its first commercial product, aimed mainly at robotics companies, in July, about two months after the talk, followed by an autonomous-driving development kit combining several sensors and, by year end, a first sample toward a commercialized automotive sensor. He names Nvidia and Infineon as partners and says the three co-founders split roles between the technical inventors of the sensing system and himself handling business and customers. The company has raised around two million dollars to date from Silicon Valley and German investors and is starting a new funding round.
Q&A highlights
Asked what role machine learning actually plays, Bahnemann concedes the sensor itself currently uses classic signal processing rather than large trained neural nets, and that the company is only now starting to train models after realizing over the past six to nine months that the data required far more preparation than expected; he positions Toposens as an enabling technology for AI rather than an AI company itself. Asked where the sensor fits in autonomous driving given its five-meter range, he calls it complementary within a full sensor stack alongside lidar and cameras, most relevant for low-speed maneuvering and parking. Asked about Tesla, he says Tesla uses twelve ultrasonic sensors and, without disclosing NDA-covered details, that a company like Tesla is very interested in the level of data Toposens can provide. Asked about broader sound applications such as detecting sirens, he says the system is largely hardware-agnostic, and confirms that standard optical-style image recognition does not work directly on the sound-derived point cloud data; new types of neural nets tailored to its patterns still need to be developed.
Quotes
For the first time ever we can understand our surroundings like bats can perceive their environment through ultrasound.
Tobias Bahnemann
Questions answered in this talk
How does Toposens' 3D ultrasound sensor work?
According to Tobias Bahnemann at Rise of AI 2019, the sensor emits high-frequency sound pulses, captures the reflections with three microphones, and calculates time of flight on its SoundVision 1 chip to build a three-dimensional point cloud, similar to how bats use echolocation.
Why is ultrasound sensing useful for autonomous driving and robotics?
According to Tobias Bahnemann, ultrasound sensing works regardless of lighting, is far cheaper than camera or lidar systems, and performs well at the close range, such as curbs and parking maneuvers, where existing optical systems often fail.
What is needed to train a reliable neural net on 3D ultrasound sensor data?
According to Tobias Bahnemann, Toposens found four prerequisites: the sensor data must be reliable and consistent across units, precise down to the millimeter, dense enough per point cloud, and available in large quantities before a proper neural net can be trained.