The present disclosure provides a method and apparatus for obtaining a map of an Internet of things device. The method is applicable to a first device with a camera and being moveable, and includes: determining a map of an area; and transmitting the map to a control system for controlling one or more IoT devices

Systems and methods for performing three-dimensional semantic parsing of indoor spaces in accordance with embodiments of the invention are disclosed. In one embodiment, a method includes receiving input data representing a three-dimensional space, determining disjointed spaces within the received data by generating a density histogram on each of a plurality of axes, determining space dividers based on the generated density histogram, and dividing the point cloud data into segments based on the determined space dividers, and determining elements in the disjointed spaces by aligning the disjointed spaces within the point cloud data along similar axes to create aligned versions of the disjointed spaces normalizing the aligned version of the disjointed spaces into the aligned version of the disjointed spaces, determining features in the disjointed spaces, generating at least one detection score, and filtering the at least one detection score to determine a final set of determined elements.

The absolute altitude of multiple floors of a building are determined using a multi-mode learning process. Processor(s) execute application program code to cause an apparatus to operate in an initial floor learning mode that includes capturing outdoor altitude estimates for the apparatus and determining, based in part on the outdoor altitude estimates, instant altitude estimates for an initial floor of the building. An absolute altitude estimate for the initial floor is determined based one the instant altitude estimates. After the absolute altitude estimate for the initial floor satisfies a first threshold certainty, the apparatus operates in a subsequent floor learning mode configured for determining respective absolute altitudes for floors of the building based on the absolute altitude estimate for the initial floor and a priori floor height information for the building, a sensor-based altitude change estimate, and/or a determined floor height for the building.

A system for generating navigation data is provided. The system, for example, may obtain route data associated with a region. The route data may include at least one route for navigation in the region. Further, the system may determine mask data associated with the at least one route or a portion thereof. The mask data may include data about whether a mask is worn or not on the at least one route or the portion thereof. Furthermore, the system may generate the navigation data for navigation in the region based on the determined mask data.

Methods, systems, and apparatus a drone for installing sensors. A method includes determining to emulate a view of a camera at a particular location with a drone, deploying the drone to the particular location, obtaining an image captured by a drone, and emulating the view of the camera with the image.

Systems and methods for operating robotic equipment in a controlled zone are presented. The system comprises one or more self-driving material-transport vehicles having at least one sensor, non-transitory computer-readable media, and a processor in communication with the at least one sensor and media. The media stores computer instructions that configure the processor to move the vehicle towards the controlled zone in a normal mode of operation, capture environmental data associated with the controlled zone using the at least one sensor, determine environmental-change data based on comparing the captured environmental data with known-good environmental data, and operating the vehicle in a safe mode of operation based on the environmental-change data.

A method for generating paths in a building space, the method including retrieving, by a processing circuit, a heatmap for the building space, generating, by the processing circuit, a plurality of vectors, the vectors defined by a first grid location and a second grid location of a plurality of grid locations, determining, by the processing circuit, a subset of vectors from the plurality of vectors based on a proximity of the plurality of vectors to a selected vector in the plurality of vectors, and combining, by the processing circuit, the subset of vectors to generate a merge location, wherein the merge location is used to adjust at least one of the first grid location or second grid location of at least one vector in the subset of vectors.

A cross reality system enables any of multiple devices to efficiently and accurately access previously persisted maps of very large scale environments and render virtual content specified in relation to those maps. The cross reality system may build a persisted map, which may be in canonical form, by merging tracking maps from the multiple devices. A map merge process determines mergibility of a tracking map with a canonical map and merges a tracking map with a canonical map in accordance with mergibility criteria, such as, when a gravity direction of the tracking map aligns with a gravity direction of the canonical map. Refraining from merging maps if the orientation of the tracking map with respect to gravity is not preserved avoids distortions in persisted maps and results in multiple devices, which may use the maps to determine their locations, to present more realistic and immersive experiences for their users.

Techniques are described for using computing devices to perform automated operations for analyzing video (or other image sequences) acquired in a defined area, as part of generating mapping information of the defined area for subsequent use (e.g., for controlling navigation of devices, for display on client devices in corresponding GUIs, etc.). The defined area may include an interior of a multi-room building, and the generated information may include a floor map of the building, such as from an analysis of some or all image frames of the video (e.g., 360° image frames from 360° video) using structure-from-motion techniques to identify objects with associated plane and normal orthogonal information, and then clustering detected planes and/or normals from multiple analyzed images to determine likely wall locations. The generating may be further performed without using acquired depth information about distances from the video capture locations to objects in the surrounding building.

Described in detail herein are systems and methods for generating computerized models of structures using geometry extraction and reconstruction techniques. The system includes a computing device coupled to a input device. The input device obtains raw data scanned by a sensor. The computing device is programmed to execute a data fusion process is applied to fuse the raw data, and a geometry extraction process is performed on the fused data to extract features such as walls, floors, ceilings, roof planes, etc. Large- and small-scale features of the structure are reconstructed using the extracted features. The large- and small-scale features are reconstructed by the system into a floor plan (contour) and/or a polyhedron corresponding to the structure. The system can also process exterior features of the structure to automatically identify condition and areas of roof damage.