Honeywell International Inc.

Carrier Corporation

Alberto Speranzon

Vijay Venkataraman

Zhiyong Dai

Andrew Stewart

Carl A Dins

Juan Hu

Ankit Tiwari

Zhiguo Ren

Blanca Florentino

Tarik Hadzic

Menouer Boubekeur

John Marchioli

Shashank Shivkumar

Naman Rawal

Marina V Blanton

Carl Arthur Dins

Shaunak D Bopardikar

Systems and methods for a tightly coupled end-to-end multi-sensor fusion with integrated compensation are described herein. For example, a system includes an inertial measurement unit that produces inertial measurements. Additionally, the system includes additional sensors that produce additional measurements. Further, the system includes one or more memory units. Moreover, the system includes one or more processors configured to receive the inertial measurements and the additional measurements. Additionally, the one or more processors are configured to compensate the inertial measurements with a compensation model stored on the one or more memory units. Also, the one or more processors are configured to fuse the inertial measurements with the additional measurements using a differential filter that applies filter coefficients stored on the one or more memory units. Further, the compensation model and the filter coefficients are stored on the one or more memory units as produced by execution of a machine learning algorithm.

Tightly coupled end-to-end multi-sensor fusion with integrated compensation

A system for localization of a safe landing zone comprises at least one image-capture device onboard an aerial vehicle, and an onboard processor coupled to the image-capture device. The processor is operative to execute instructions to perform a method that comprises: receive, from the image-capture device, two or more overlapping images of a landscape underneath the aerial vehicle; generate, based on the overlapping images, a landing zone heatmap of the landscape; identify, based on the landing zone heatmap, one or more regions of the landscape having potential landing zones and obstacles; and determine a location of a safe landing zone using a distance transform of the one or more regions of the landscape. The location of the safe landing zone is in an area within one of the potential landing zones that is farthest from the obstacles. The location of the safe landing zone is then stored in a database.

System and method for localization of safe zones in dense depth and landing quality heatmaps

Disclosed are methods and systems for generating three-dimensional reconstructions of environments. A system, for example, may include a housing having an image sensor directed in a first direction and a distance sensor directed in a second direction and a control unit including a processor and a memory storing instructions. The processor may be configured to execute the instructions to: generate a first 3D model of an environment; generate a plurality of revolved 3D models by revolving the first 3D model relative to the image sensor to a plurality of positions within a predetermined angular range; match a set of distance values to one of the revolved 3D models; determine an angular position of the second direction relative to the first direction; and generate a 3D reconstruction of the environment.

Systems and methods for generating three-dimensional reconstructions of environments

An unmanned aerial vehicle (UAV) navigation system is configured to automatically identify a suitable UAV landing site, for example, in forced-landing (e.g., emergency) scenarios. In some examples, the system receives two or more overlapping images depicting a landscape underneath an airborne unmanned aerial vehicle (UAV); generates, based on the two or more overlapping images, a depth map for the landscape; identifies, based on the depth map, regions of the landscape having a depth variance below a threshold value; determines, for each of the regions of the landscape having a depth variance below the threshold value, a landing zone quality score indicative of the depth variance and a semantic type of the region of the landscape; identifies, based on the landing zone quality scores, a suitable location for landing the UAV; and causes the UAV to automatically navigate toward and land on the suitable location.

Automatic safe-landing-site selection for unmanned aerial systems

An access control request manager based on learning profile based access pathways in a physical access control system (PACS). The access control request manager including an access pathways learning module configured to determine an reachability graph associated with each resource in the PACS, and a permissions request module, the permissions request module including an access request user interface configured to permit a user or an administrator, to provide a request for a permission to permit the user access to, or revoke the user's access to, a resource in the PACS. The permissions request module determines if the requested permission is already granted to the user, computes a pathway to reach the requested resource based on the reachability graph, suggests a permission, if needed, required to satisfy the request based on the computed pathway, and if approved, adds the suggested permission to any permissions granted to the user.

Access control request manager based on learning profile-based access pathways

A method comprises: maintaining a database () of access control events; dividing a portion () of the database into shares (A,B,C); passing the respective shares to respective third party servers (A,B,C); processing the shares in the respective third party servers; passing output of the processing to a further server () in common; and processing the output on the further server.

Query-aware privacy for access control data analytics

An Inertial Measurement Unit (IMU), method, and navigation system are disclosed. For example, the method includes receiving a plurality of sensor values from an IMU, loading the plurality of sensor values and a plurality of true sensor values into a deep learning algorithm, and training the deep learning algorithm to enhance the accuracy of the plurality of sensor values utilizing the plurality of true sensor values.

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Maple Grove, MN, United States of America

Alberto Speranzon