Abstract: Methods and systems for use with an automated storage and retrieval system (ASRS) are described. In some embodiments, a model may be generated using a simplified three dimensional model of the storage facility where the model may include functionalized interconnected volumes and/or surfaces. During operation, an ASRS may include at least one robot, which can move along surfaces, hold a payload (e.g., containers, objects, etc.), and interact with the environment of the storage facility to facilitate movement and handling of the payload within the storage facility. In some embodiments, the robot may obtain and use a map of the storage facility that is generated using the disclosed models for controlling operation of the robot based at least in part on a location of the robot within the storage facility and the functionality of that location defined in the map of the storage facility.

Abstract: A method for processing digital media is described. In one example embodiment, the method may include detecting an unknown object in a video frame, receiving inputs representing probable identities of the unknown object in the video frame from various sources, and associating each input with the unknown object detected in the video frame. The received inputs may be processed, compared with reference data and, based on the comparison, probable identities of the object associated with the input derived. The method may further include retrieving a likelihood of the input to match the unknown object from historical data and producing weights corresponding to the inputs, fusing the inputs and the relative weight associated with each input, and identifying the unknown object based on a comparison of the weighted distances from the unknown identify to a reference identity.

Abstract: A method for processing digital media is described. In one example embodiment, the method may include detecting an unknown object in a video frame, receiving inputs representing probable identities of the unknown object in the video frame from various sources, and associating each input with the unknown object detected in the video frame. The received inputs may be processed, compared with reference data and, based on the comparison, probable identities of the object associated with the input derived. The method may further include retrieving a likelihood of the input to match the unknown object from historical data and producing weights corresponding to the inputs, fusing the inputs and the relative weight associated with each input, and identifying the unknown object based on a comparison of the weighted distances from the unknown identify to a reference identity.