Abstract: Solutions are provided for auto-labeling sensor data for machine learning (ML). An example includes: determining a platform's own position; recording, from a sensor aboard the platform, sensor data comprising a sensor image; receiving position data for at least one intruder object (e.g., a nearby airborne object); based at least on the position data for the intruder object and the platform's position, determining a relative position and a relative velocity of the intruder object; based at least on the relative position and a relative velocity of the intruder object and a field of view of the sensor, determining an expected position of the intruder object in the sensor image; labeling the sensor image, wherein the labeling comprises annotating the sensor image with a region of interest and an object identification; and training an artificial intelligence (AI) model using the labeled sensor image.

Abstract: A method of supporting an aircraft approaching a runway is provided. Example implementations involve receiving a sequence of images, captured by a camera onboard the aircraft. Example implementations involve applying a received image to a machine learning model trained to detect the runway or a runway marking in the image, and to produce a mask that includes a segment of pixels of the image assigned to an object class for the runway or marking. Example implementations may also involve applying the mask to a corner detector to detect interest points on the mask and match the interest points to corresponding points on the runway or the marking that have known runway-framed local coordinates. Example implementations may also involve performing a perspective-n-point estimation to determine a current pose estimate of the aircraft relative to the runway or the marking and outputting the current pose estimate for use during final approach.

Abstract: A method for aligning a removable sensor on a vehicle includes connecting the removable sensor to a sensor mounting device. The method further includes connecting a connector of an alignment apparatus to either (i) the removable sensor such that a spatial reference component of the alignment apparatus has a known position and orientation relative to a current position and orientation of the removable sensor or (ii) a fixed connection location on the vehicle such that the spatial reference component indicates a desired position and orientation of the removable sensor. In addition the method includes adjusting the current position and orientation of the removable sensor by reference to the alignment apparatus to cause the current position and orientation of the removable sensor to match the desired position and orientation of the removable sensor.

Abstract: Solutions are provided for auto-labeling sensor data for machine learning (ML). An example includes: determining a platform's own position; recording, from a sensor aboard the platform, sensor data comprising a sensor image; receiving position data for at least one intruder object (e.g., a nearby airborne object); based at least on the position data for the intruder object and the platform's position, determining a relative position and a relative velocity of the intruder object; based at least on the relative position and a relative velocity of the intruder object and a field of view of the sensor, determining an expected position of the intruder object in the sensor image; labeling the sensor image, wherein the labeling comprises annotating the sensor image with a region of interest and an object identification; and training an artificial intelligence (AI) model using the labeled sensor image.

Abstract: A method is provided for supporting an aircraft approaching a runway on an airfield. The method includes receiving a sequence of images of the airfield, captured by a camera onboard the aircraft approaching the runway. For at least one image of the sequence of images, the method includes applying the image(s) to a machine learning model trained to predict a pose of the aircraft relative to the runway. The machine learning model is configured to map the image(s) to the pose based on a training set of labeled images with respective ground truth poses of the aircraft relative to the runway. The pose is output as a current pose estimate of the aircraft relative to the runway for use in at least one of monitoring the current pose estimate, generating an alert based on the current pose estimate, or guidance or control of the aircraft on a final approach.

Abstract: A method of supporting an aircraft approaching a runway is provided. Example implementations involve receiving a sequence of images, captured by a camera onboard the aircraft. Example implementations involve applying a received image to a machine learning model trained to detect the runway or a runway marking in the image, and to produce a mask that includes a segment of pixels of the image assigned to an object class for the runway or marking. Example implementations may also involve applying the mask to a corner detector to detect interest points on the mask and match the interest points to corresponding points on the runway or the marking that have known runway-framed local coordinates. Example implementations may also involve performing a perspective-n-point estimation to determine a current pose estimate of the aircraft relative to the runway or the marking and outputting the current pose estimate for use during final approach.

Abstract: A method for aligning a removable sensor on a vehicle includes connecting the removable sensor to a sensor mounting device. The method further includes connecting a connector of an alignment apparatus to either (i) the removable sensor such that a spatial reference component of the alignment apparatus has a known position and orientation relative to a current position and orientation of the removable sensor or (ii) a fixed connection location on the vehicle such that the spatial reference component indicates a desired position and orientation of the removable sensor. In addition the method includes adjusting the current position and orientation of the removable sensor by reference to the alignment apparatus to cause the current position and orientation of the removable sensor to match the desired position and orientation of the removable sensor.

Abstract: Systems and methods of aligning removable sensors mounted on a vehicle based upon sensor output of such sensors are described. Sensor output is collected from a removable sensor (e.g., a digital camera), and a representation of such sensor output is generated (e.g., a digital image). The representation of the sensor output is compared against a spatial template (e.g., a digital mask overlaid on the representation) to determine whether external references in the representation align with corresponding reference indicators in the spatial template. When alignment is required, the removable sensor is aligned by one or both of the following until the external references in a representation of sensor data at an updated current location align with the corresponding reference indicators in the spatial template: (i) adjusting the position of the removable sensor on the vehicle or (ii) adjusting the representation of the sensor output to simulate such repositioning.