System and method for mapping vectorization and navigation system using convolutional neural network
AssigneeAliensense Limited
InventorsAndrei Rychazhnikov, Alex Lapir
Life of this patent
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Abstract
An onboard navigation system for vehicles in GPS-denied environments using image-based mapping. A mapping vectorization and navigation system uses a convolutional neural network (CNN) to improve the speed of recognition, orientation, and navigation while avoiding the use of GPS/GNSS signals.
Claims20
A method for navigating an autonomous vehicle in a location without the use of real-time satellite data, the method comprising:
- providing a reference vectored map of the location, in a storage medium onboard the autonomous vehicle;
- capturing a raster image of a landscape within the location using a imaging system onboard the autonomous vehicle;
- creating a filtered image by applying an edge detection filter to the raster image to detect edges and define initial vector borders within the raster image of the landscape;
- processing, under control of a processor onboard the autonomous vehicle, the filtered image through a first convolutional neural network (CNN) to produce a first vectored map;
- comparing, under control of the processor onboard the autonomous vehicle, the generated first vectored map with the reference vectored map to determine the current position of the autonomous vehicle within the location;
- and
- changing the position of the autonomous vehicle based on the determined current position of the autonomous vehicle.
A system for navigating an autonomous vehicle in a location without using real-time satellite data, the system comprising:
- an autonomous vehicle;
- a processor onboard the autonomous vehicle;
- an onboard memory operably coupled to the processor;
- a nonvolatile storage medium for storing raster images and reference vectored maps of the location;
- an optical system comprising a camera, configured for capturing raster images of a landscape within the location;
- an image processing module comprising an edge detection filter and one or more convolutional neural networks (CNN), wherein the edge detection filter is configured for detecting edges and defining initial vector borders within the raster images and wherein the one or more CNNs are configured for producing a first vectored map with corresponding detected sets of landscape by processing the filtered images through one or more CNNs;
- a control block, operably connected to the image processing module, configured for comparing the generated first vectored map with the previously stored reference vectored maps to determine a current position within the location;
- and
- a transmitter, configured for sending the current position within the location to a destination.
A method for determining the position of an autonomous vehicle without the use of real-time satellite data satellite data, the method comprising:
- providing a reference vectored map of a location, in a storage medium onboard the autonomous vehicle;
- capturing a raster image of a landscape within the location using a imaging system onboard the autonomous vehicle;
- creating a filtered image by applying an edge detection filter to the raster image to detect edges and define initial vector borders within the raster image of the landscape;
- processing, under control of a processor onboard the autonomous vehicle, the filtered image through a first convolutional neural network (CNN) to produce a first vectored map;
- comparing, under control of the processor onboard the autonomous vehicle, the generated first vectored map with the reference vectored map to determine the current position of the autonomous vehicle within the location.
Dependencies are inferred from the claim text.