Abstract: An aircraft collision avoidance system includes a plurality of three-dimensional (3D) light detection and ranging (LIDAR) sensors, a plurality of sensor processors, a plurality of transmitters, and a display device. Each 3D LIDAR sensor is enclosed in an aircraft exterior lighting fixture that is configured for mounting on an aircraft, and is configured to sense objects within its field-of-view and supply sensor data. Each sensor processor receives sensor data and processes the received sensor data to determine locations and physical dimensions of the sensed objects. Each transmitter receives the object data, and is configured to transmit the received object data. The display device receives and fuses the object data transmitted from each transmitter, fuses the object data and selectively generates one or more potential obstacle alerts based on the fused object data.

Abstract: An aircraft collision avoidance system includes a plurality of three-dimensional (3D) light detection and ranging (LIDAR) sensors, a plurality of sensor processors, a plurality of transmitters, and a display device. Each 3D LIDAR sensor is enclosed in an aircraft exterior lighting fixture that is configured for mounting on an aircraft, and is configured to sense objects within its field-of-view and supply sensor data. Each sensor processor receives sensor data and processes the received sensor data to determine locations and physical dimensions of the sensed objects. Each transmitter receives the object data, and is configured to transmit the received object data. The display device receives and fuses the object data transmitted from each transmitter, fuses the object data and selectively generates one or more potential obstacle alerts based on the fused object data.

Abstract: Systems and methods are provided for selecting landmarks for navigation. In one embodiment, a system comprises an IMU that provides inertial measurements for a vehicle and at least one image sensor that acquires measurements of the vehicle's environment. The system also comprises a processing unit that calculates a navigation solution for the vehicle based on the inertial measurements, identifies a plurality of landmarks in the acquired measurements, and identifies a plurality of usable landmarks from the plurality of landmarks. The processing unit also selects a subset of useable landmarks from the plurality of useable landmarks such that the subset of landmarks has a smaller dilution of precision (DOP) than other possible subsets of landmarks from the plurality of useable landmarks, and calculates an updated navigation solution from the subset of landmarks. The DOP is an amplification factor of measurement errors derived from the geometry of the subset of useable landmarks.

Abstract: A method comprises receiving a first frame from at least one imaging device, receiving a second frame from the at least one imaging device, analyzing at least a portion of the first frame and at least a portion of the second frame, and indicating when at least one of a zero velocity update and a zero attitude update should be performed based on at least in part on the analysis of the at least a portion of the first frame and the at least a portion of the second frame. The first frame is captured at a first vantage point and the second frame is captured at a second vantage point.

Abstract: Systems and methods are provided for selecting landmarks for navigation. In one embodiment, a system comprises an IMU that provides inertial measurements for a vehicle and at least one image sensor that acquires measurements of the vehicle's environment. The system also comprises a processing unit that calculates a navigation solution for the vehicle based on the inertial measurements, identifies a plurality of landmarks in the acquired measurements, and identifies a plurality of usable landmarks from the plurality of landmarks. The processing unit also selects a subset of useable landmarks from the plurality of useable landmarks such that the subset of landmarks has a smaller dilution of precision (DOP) than other possible subsets of landmarks from the plurality of useable landmarks, and calculates an updated navigation solution from the subset of landmarks. The DOP is an amplification factor of measurement errors derived from the geometry of the subset of useable landmarks.

Abstract: An example embodiment includes a method for identifying true feature matches from a plurality of candidate feature matches for vision based navigation. A weight for each of the plurality of candidate feature matches can be set. The method also includes iteratively performing for N iterations: calculating a fundamental matrix for the plurality of candidate feature matches using a weighted estimation that accounts for the weight of each of the plurality of candidate feature matches; calculating a distance from the fundamental matrix for each of the plurality of candidate feature matches; and updating the weight for each of the plurality of candidate feature matches as a function of the distance for the respective candidate feature match. After N iterations candidate feature matches having a distance less than a distance threshold can be selected as true feature matches.

Abstract: An example embodiment includes a method for identifying true feature matches from a plurality of candidate feature matches for vision based navigation. A weight for each of the plurality of candidate feature matches can be set. The method also includes iteratively performing for N iterations: calculating a fundamental matrix for the plurality of candidate feature matches using a weighted estimation that accounts for the weight of each of the plurality of candidate feature matches; calculating a distance from the fundamental matrix for each of the plurality of candidate feature matches; and updating the weight for each of the plurality of candidate feature matches as a function of the distance for the respective candidate feature match.

Abstract: A method comprises receiving a first frame from at least one imaging device, receiving a second frame from the at least one imaging device, analyzing at least a portion of the first frame and at least a portion of the second frame, and indicating when at least one of a zero velocity update and a zero attitude update should be performed based on at least in part on the analysis of the at least a portion of the first frame and the at least a portion of the second frame. The first frame is captured at a first vantage point and the second frame is captured at a second vantage point.

Abstract: A method for preprocessing sensor data applicable to sensor fusion for one or more sensors mounted on a vehicle is presented. The method comprises obtaining sensor data relating to common obstacles between the vision sensors and a lidar sensor, calculating range and azimuth values for the common obstacles from the lidar sensor data, and calculating range and azimuth values for the common obstacles from the vision sensor data. Then the method correlates the lidar sensor data pertaining to the common obstacles with the vision sensors pixels, formulates translations between the range values of the common obstacles and the one or more sensor tilt parameters, and performs recursive least squares to estimate an estimated sensor tilt for the vision sensors that can reduce range errors in the vision sensors.