Abstract: An image processing system includes at least two three-dimensional sensors and a controller. Each of the sensors being for illuminating a corresponding field of view of the sensor, and generating an output array of pixelwise values indicative of distances to illuminated objects in the field of view, each sensor being configured to generate illumination at least at two different frequencies so that objects in the corresponding field of view are illuminated at the two different frequencies. The controller is communicably connected to the at least two sensors to receive pixelwise data from each sensor embodying intensity and distance information from the illuminated objects. The controller is configured to disambiguate a distance to each of the illuminated objects, resolve error in the received pixelwise data due to periodic distance ambiguity, and determine corrected pixelwise values indicative of true distance via each sensor illumination at the at least two different frequencies.

Abstract: An apparatus includes a memory and a hardware processor. The hardware processor determines one or more of a weight of an item, a packaging type of the item, a packaging material of the item, a barcode of the item, a rigidity of the item, or a physical response of the item to being lifted and determines a visual appearance of the item and a shape or size of the item. The hardware processor also compares, using a machine learning model, the determined characteristics of the item to a manifest for the container. The manifest identifies a plurality of items in the container. The hardware processor determines, using the machine learning model, an identity of the item based on comparing the determined characteristics of the item to the manifest.

Abstract: Systems and methods for sensor registration for a workcell including a floor are disclosed. In some embodiments, a disclosed method comprises: disposing a plurality of three dimensional (3D) image sensors proximate to the workcell; acquiring, with the plurality of 3D image sensors, a plurality of 3D images of the workcell, wherein at least one of the plurality of 3D images includes at least a portion of the floor; determining, based on a first user selection of a region of a first image of the plurality of 3D images of the workcell, a first floor point indicating a first portion of the floor in the first image; computing a first floor plane representing the floor based on the first floor point; and registering the 3D image sensors to each other and to the workcell based at least in part on the plurality of 3D images and the first floor plane.

Abstract: Image sensors distributed about a workcell including industrial machinery are registered using a registration object and an information tag associated therewith. The tag contains information specifying the location of the object and/or the pose of the object. This information is acquired along with images of the registration object, and the sensors are registered based at least in part on the images and the acquired information.

Abstract: A system including: one or more processors; and at least one memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to: build one or more event-predictive models based on historical data; gather current data comprising at least one from among current and forecasted weather, current and planned external events, and current and expected delay factors; process the gathered current data with the built one or more event-predictive models to predict respective timeframes and locations for one or more future emergency events; identify, based on the timeframes and locations for one or more future emergency events and current and scheduled resource locations and statuses for a plurality of resources, a resource deficient area; and reposition one or more of the plurality of resources to a location corresponding to the resource deficient area.

Abstract: A computer implemented method measures a performance of surveillance system. A site model, a sensor model and a traffic model are selected respectively from a set of site models, a set of sensor models, and a set of traffic models to form a surveillance model. Based on the surveillance model surveillance signals are generated. Performance of the surveillance system is evaluated according to qualitative surveillance goals and the surveillance signals to determine a value of a quantitative performance metric of the surveillance system.

Abstract: A computer implemented method measures a performance of surveillance system. A site model, a sensor model and a traffic model are selected respectively from a set of site models, a set of sensor models, and a set of traffic models to form a surveillance model. Based on the surveillance model surveillance signals are generated. Performance of the surveillance system is evaluated according to qualitative surveillance goals and the surveillance signals to determine a value of a quantitative performance metric of the surveillance system.