{"id":3806,"date":"2023-06-15T14:38:08","date_gmt":"2023-06-15T11:38:08","guid":{"rendered":"https:\/\/aire-edih.eu\/?post_type=project&p=3806"},"modified":"2024-06-20T08:29:58","modified_gmt":"2024-06-20T05:29:58","slug":"2023-vision-based-off-road-navigation-with-geographical-hints","status":"publish","type":"project","link":"https:\/\/aire-edih.eu\/en\/project\/2023-vision-based-off-road-navigation-with-geographical-hints\/","title":{"rendered":"2023: Testing vision-based off-road navigation with geographical hints for autonomous mobile robots (operating in wild nature)"},"content":{"rendered":"

\"\"<\/p>\n

The goal for the project is to demonstrate robot navigation from point A to point B in an off-road environment using a possibly outdated map as a navigational hint. For testing the final\u00a0result the public orienteering events are used, where the robot must follow the given\u00a0orienteering track from start to finish, using only an orienteering map or satellite photo. In\u00a0the first phase of the project a dataset is collected that is used to train two machine learning\u00a0models: the first proposes new waypoints based on local sensor information, and the\u00a0second uses the map to evaluate which of those waypoints leads to the final destination\u00a0fastest. The second phase of the project implements the navigation system on a real robot.<\/p>\n

github<\/span><\/a><\/div>\n","protected":false},"excerpt":{"rendered":"

The goal for the project is to demonstrate robot navigation from point A to point B in an off-road environment using a possibly outdated map as a navigational hint. 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