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Towards autonomous navigation, localization, and mapping

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.

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Webinar recap: Silo AI x NVIDIA webinar – Situational awareness for vessels, Case: Groke Technologies

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.

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Sensor fusion

The how & why of sensor fusion

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.

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Physical AI Systems — AI with a body

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.

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Implementing end-to-end scalable MLOps for a computer vision product

The overall goal of MLOps is to make the process of productizing ML models smoother. MLOps applies the DevOps techniques, concepts and practices to machine learning systems with an increased demand to take care of aspects around the activities, such as data versioning, data lineage and data quality. MLOps responses to an increased need for model observability and monitoring model performance.

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