Technological Core
The pipeline combines both classical methods and deep learning techniques. We utilise neural networks that we train ourselves to tackle tasks such as image retrieval and the extraction of key points from images.
To support this, we've developed and continuously expand our own datasets, gathered through thousands videos taken in various cities worldwide.
Neural networks play a key role in both processes: during real-time operations within our Visual Positioning System (VPS) and in offline data processing as a part of our Spatial Mapping Pipeline.
Storing map data in the cloud has the benefit of eliminating the need for users to download map data to their devices. It also allows us to continually enhance our maps and algorithms without requiring any action from our clients.
The pipeline combines both classical methods and deep learning techniques. We utilise neural networks that we train ourselves to tackle tasks such as image retrieval and the extraction of key points from images.
To support this, we've developed and continuously expand our own datasets, gathered through thousands videos taken in various cities worldwide.
Neural networks play a key role in both processes: during real-time operations within our Visual Positioning System (VPS) and in offline data processing as a part of our Spatial Mapping Pipeline.
Storing map data in the cloud has the benefit of eliminating the need for users to download map data to their devices. It also allows us to continually enhance our maps and algorithms without requiring any action from our clients.The pipeline combines both classical methods and deep learning techniques. We utilise neural networks that we train ourselves to tackle tasks such as image retrieval and the extraction of key points from images.
To support this, we've developed and continuously expand our own datasets, gathered through thousands videos taken in various cities worldwide.
Neural networks play a key role in both processes: during real-time operations within our Visual Positioning System (VPS) and in offline data processing as a part of our Spatial Mapping Pipeline.
Storing map data in the cloud has the benefit of eliminating the need for users to download map data to their devices. It also allows us to continually enhance our maps and algorithms without requiring any action from our clients.
The pipeline combines both classical methods and deep learning techniques. We utilise neural networks that we train ourselves to tackle tasks such as image retrieval and the extraction of key points from images.
To support this, we've developed and continuously expand our own datasets, gathered through thousands videos taken in various cities worldwide.
Neural networks play a key role in both processes: during real-time operations within our Visual Positioning System (VPS) and in offline data processing as a part of our Spatial Mapping Pipeline.
Storing map data in the cloud has the benefit of eliminating the need for users to download map data to their devices. It also allows us to continually enhance our maps and algorithms without requiring any action from our clients.
The pipeline combines both classical methods and deep learning techniques. We utilise neural networks that we train ourselves to tackle tasks such as image retrieval and the extraction of key points from images.
To support this, we've developed and continuously expand our own datasets, gathered through thousands videos taken in various cities worldwide.
Neural networks play a key role in both processes: during real-time operations within our Visual Positioning System (VPS) and in offline data processing as a part of our Spatial Mapping Pipeline.
Storing map data in the cloud has the benefit of eliminating the need for users to download map data to their devices. It also allows us to continually enhance our maps and algorithms without requiring any action from our clients.
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