Honeywell International Inc.

Shrikant P Rao

Yunqian Ma

Vibhor L Bageshwar

Sunit Kumar Saxena

Vijay Venkataraman

Sara Susca

Niranjan Kalyandurg

Viswanath Talasila

Abhishek Tyagi

Somnath S G Ganguly

Systems and methods for integrity monitoring of odometry measurements within a navigation system are provided herein. In certain embodiments, a system includes imaging sensors that generate image frames from optical inputs. The system also includes a GNSS receiver that provides pseudorange measurements; and computational devices that receive the image frames and the pseudorange measurements. Further, the computational devices compute odometry information from image frames acquired at different times; and calculate a full solution for the system based on the pseudorange measurements and the odometry information. Additionally, the computational devices calculate sub-solutions for the system based on subsets of the pseudorange measurements and the odometry information, wherein one of the sub-solutions is not based on the odometry information. Moreover, the computational device determines the integrity of the pseudorange measurements, the odometry information, and a position estimated by the navigation system based on a comparison of the full and sub-solutions.

Integrity monitoring of odometry measurements within a navigation system

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.

Lidar-based aircraft collision avoidance system

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.

Systems and methods for landmark selection for navigation

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.

Vision based zero velocity and zero attitude rate update

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.

Identifying true feature matches for vision based navigation

A wind energy converter has a generator adapted to be coupled directly to a wind turbine without need for a mechanical gear unit. A rectifier is coupled to the generator and a converter is coupled to the rectifier to provide a regulated DC bus voltage as a function of a controlled duty cycle. An inverter is coupled to the converter for providing a regulated AC output to a load.

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Bengaluru, India

Shrikant P Rao