Machine learning operations (MLOps) is about making machine learning development activities systematic and connecting the machine learning projects to the company’s business and IT infrastructure while bringing automation to the
Silo AI offers a modular AI-powered Visual Quality Control solution for deploying AI workflows on Intel® hardware. The solution runs on edge devices and can be deployed on the hardware and cloud infrastructure of choice.
Deep learning-based sensor fusion for situational awareness is a notably different approach from classic mathematical modeling. While the underlying core tasks of perception, prediction, and planning remain the same, deep
We’re thrilled to welcome Mikael Lillback to our business development team as a Business Development Executive. At Silo AI, Mikael will focus on smart vehicles and the automotive ecosystem to
Autonomous navigation, localization, and mapping mean the ability of the autonomous system to create a map of the surrounding environment, localize the machine on that map and make the navigation planning accordingly. These features are crucial in the development of autonomous machinery, vehicles, and vessels, and are often enabled by techniques such as sensor fusion.
Groke Technologies is paving the way for autonomous navigation solutions in the marine industry. On June 2, 2021, we had the opportunity to learn about the future of the marine industry with Groke Technologies, a Finnish marine company. With the help of Silo AI experts and the use of NVIDIA’s products, Groke is utilizing sensor fusion technologies to achieve better situational awareness of a vessel’s surroundings.
Sensor fusion in a nutshell is about combining the information from multiple different sensors in order to obtain a more accurate picture than any single sensor could provide by itself. From a theoretical point of view, sensor fusion is firmly based on understanding probability as a state of knowledge, which allows us to combine and manipulate different sources of information via the methods of Bayesian statistics. From a more practical point of view, real-time numerical sensor fusion is viable primarily due to the fact that Bayesian combinations of Gaussian probability densities yield Gaussian densities as a result. Thus if we can approximate our current state of knowledge and the incoming information using Gaussian probability densities, we can leverage analytical formulae and numerical linear algebra. This allows us to efficiently compute our updated state of knowledge.
We’re excited to announce that Tarmo Pajunen is joining our fast-growing business development team as a Business Development Executive. With his extensive background in business transformation, data strategy, and digitalization, he will focus on increasing the adoption of AI, particularly in the industrial sector.
In this article, I will cover the most common use cases I’ve seen for sensor fusion, and gather some of my own experiences in working with modern AI-driven sensor fusion techniques in the maritime and automotive industries.
An embodied AI system can actively perceive the world around them in order to gather information through sensors such as vision, process this information on different cognitive levels, both autonomously and in interaction with a human, and finally make plans or decisions based on the outcome. In the autonomous vehicle case, the internal processing consists of planning future driving actions in interaction with the surrounding world and possibly also with a driver.
Join 5000+ subscribers who read the Silo AI monthly newsletter
|
