Gm Global Technolgoy Operations, Inc.

Daniel I Dobkin

Michael Slutsky

A vehicle, navigation system of the vehicle, and method for observing an environment of the vehicle. The navigation system includes a sensor and a processor. The sensor obtains a detection from an object located in an environment of the vehicle at a with-detection direction with respect to the sensor that includes the detection. The processor determines a no-detection direction that does not include the detection, assigns an empty space likelihood to an occupancy grid of the sensor along the no-detection direction, and generates a map of the environment using the occupancy grid.

Generalized 3D inverse sensor model

A vehicle pose determining system and method for accurately estimating the pose of a vehicle (i.e., the location and/or orientation of a vehicle). The system and method use a form of sensor fusion, where output from vehicle dynamics sensors (e.g., accelerometers, gyroscopes, encoders, etc.) is used with output from vehicle radar sensors to improve the accuracy of the vehicle pose data. Uncorrected vehicle pose data derived from dynamics sensor data is compensated with correction data that is derived from occupancy grids that are based on radar sensor data. The occupancy grids, which are 2D or 3D mathematical objects that are somewhat like radar-based maps, must correspond to the same geographic location. The system and method use mathematical techniques (e.g., cost functions) to rotate and shift multiple occupancy grids until a best fit solution is determined, and the best fit solution is then used to derive the correction data that, in turn, improves the accuracy of the vehicle pose data.

Vehicle pose determining system and method

A vehicle, system and method of mapping the environment is disclosed. The system includes a sensor and a processor. The sensor is configured to obtain a detection from an object in an environment surrounding the vehicle. The processor is configured to compute a plurality of radial components and a plurality of angular components for a positive inverse sensor model (ISM) of an occupancy grid, select a radial component corresponding to a range of the detection from the plurality of radial components and selecting an angular component corresponding to an angle of the detection from the plurality of angular components, multiply the selected radial component and the selected angular component to create an occupancy grid for the detection, and map the environment using the occupancy grid.

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Tel Aviv, Israel

Daniel I Dobkin