Abstract: According to embodiments of the present disclosure is an automatic floor preparation or maintenance system. The system uses a laser rangefinder to map a workspace, while a path planning module uses the map to determine a path for the concrete polishing vehicle to travel. A propulsion system moves the vehicle along the determined path. A user interface allows an operator to remotely monitor the autonomous operations.

Abstract: According to embodiments of the present disclosure is an automatic floor preparation or maintenance system. The system uses a laser rangefinder to map a workspace, while a path planning module uses the map to determine a path for the concrete polishing vehicle to travel. A propulsion system moves the vehicle along the determined path. A user interface allows an operator to remotely monitor the autonomous operations.

Abstract: According to embodiments of the present disclosure is an automatic floor preparation or maintenance system. The system uses a laser rangefinder to map a workspace, while a path planning module uses the map to determine a path for the concrete polishing vehicle to travel. A propulsion system moves the vehicle along the determined path. A user interface allows an operator to remotely monitor the autonomous operations.

Abstract: According to embodiments of the present disclosure is an automatic floor preparation or maintenance system. The system uses a laser rangefinder to map a workspace, while a path planning module uses the map to determine a path for the concrete polishing vehicle to travel. A propulsion system moves the vehicle along the determined path. A user interface allows an operator to remotely monitor the autonomous operations.

Abstract: According to embodiments of the present disclosure is an automatic floor preparation or maintenance system. The system uses a laser rangefinder to map a workspace, while a path planning module uses the map to determine a path for the concrete polishing vehicle to travel. A propulsion system moves the vehicle along the determined path. A user interface allows an operator to remotely monitor the autonomous operations.

Abstract: A system and method of identifying a position of a crop row in a field, where an image of two or more crop rows is transmitted to a vision data processor. The vision data processor defines a candidate scan line profile for a corresponding heading and pitch associated with a directional movement of a vehicle, for example, traversing the two or more crop rows. The candidate scan line profile comprises an array of vector quantities, where each vector quantity comprises an intensity value and a corresponding position datum. A preferential scan line profile in a search space about the candidate scan line profile is determined, and the candidate scan line profile is identified as a preferential scan line profile for estimating a position (e.g., peak variation) of one or more crop rows if a variation in the intensity level of the candidate scan line profile exceeds a threshold variation value.

Abstract: A system and method of identifying a position of a crop row in a field, where an image of two or more crop rows is transmitted to a vision data processor. A preferential scan line profile in a search space about a candidate scan line profile is determined, and the candidate scan line profile is identified as a preferential scan line profile for estimating a position (e.g., peak variation) of one or more crop rows if a variation in the intensity level of the candidate scan line profile exceeds a threshold variation value. Alternatively, a position datum associated with a highest intensity value within the array of vector quantities can be selected as being indicative of a candidate position of a crop row. The candidate position is then identified as a preliminary row position if a variation in intensity level of the candidate scan line profile exceeds a threshold variation value.

Abstract: A system and method of identifying a position of a crop row in a field, where an image of two or more crop rows is transmitted to a vision data processor. The vision data processor defines a candidate scan line profile for a corresponding heading and pitch of associated with a directional movement of a vehicle, for example, traversing the two or more crop rows. The candidate scan line profile comprises an array of vector quantities, where each vector quantity comprises an intensity value and a corresponding position datum. A preferential scan line profile in a search space about the candidate scan line profile is determined, and the candidate scan line profile is identified as a preferential scan line profile for estimating a position (e.g., peak variation) of one or more crop rows if a variation in the intensity level of the candidate scan line profile exceeds a threshold variation value.

Abstract: A system and method of identifying a position of a crop row in a field, where an image of two or more crop rows is transmitted to a vision data processor. The vision data processor defines a candidate scan line profile for a corresponding heading and pitch of associated with a directional movement of a vehicle, for example, traversing the two or more crop rows. The candidate scan line profile comprises an array of vector quantities, where each vector quantity comprises an intensity value and a corresponding position datum. A preferential scan line profile in a search space about the candidate scan line profile is determined, and the candidate scan line profile is identified as a preferential scan line profile for estimating a position (e.g., peak variation) of one or more crop rows if a variation in the intensity level of the candidate scan line profile exceeds a threshold variation value.

Abstract: A system and method of identifying a position of a crop row in a field, where an image of two or more crop rows is transmitted to a vision data processor. The vision data processor defines a candidate scan line profile for a corresponding heading and pitch associated with a directional movement of a vehicle, for example, traversing the two or more crop rows. The candidate scan line profile comprises an array of vector quantities, where each vector quantity comprises an intensity value and a corresponding position datum. A preferential scan line profile in a search space about the candidate scan line profile is determined, and the candidate scan line profile is identified as a preferential scan line profile for estimating a position (e.g., peak variation) of one or more crop rows if a variation in the intensity level of the candidate scan line profile exceeds a threshold variation value.

Abstract: A system and method of identifying a position of a crop row in a field, where an image of two or more crop rows is transmitted to a vision data processor. A preferential scan line profile in a search space about a candidate scan line profile is determined, and the candidate scan line profile is identified as a preferential scan line profile for estimating a position (e.g., peak variation) of one or more crop rows if a variation in the intensity level of the candidate scan line profile exceeds a threshold variation value. Alternatively, a position datum associated with a highest intensity value within the array of vector quantities can be selected as being indicative of a candidate position of a crop row. The candidate position is then identified as a preliminary row position if a variation in intensity level of the candidate scan line profile exceeds a threshold variation value.

Abstract: A method for detecting an obstacle in a spatial area is disclosed. The method includes scanning the spatial area, classifying each reflection point in the spatial area as one of an obstacle reflection point and a non-obstacle reflection point, fusing the obstacle reflection points, and generating statistics associated with the obstacle.

Abstract: A method for detecting an obstacle in a spatial area is disclosed. The method includes scanning the spatial area, classifying each reflection point in the spatial area as one of an obstacle reflection point and a non-obstacle reflection point, fusing the obstacle reflection points, and generating statistics associated with the obstacle.