Abstract: A method and a device for determining motion of a device are described. In an example, a plurality of points-of-interest (POIs) are identified in a pedestrian area of an indoor space and, with each POI, a probabilistic representation of motion transition of a device from the POI is associated. The probabilistic representation of motion transition is indicative of a probability distribution of likely directions of motion that the device can exhibit from the POI. Further, a map of the indoor space is generated based on the association and the map is usable for ascertaining motion of the device in the indoor space.

Abstract: Examples of generating a map for an indoor space are described. In one example, a raw digital representation of the indoor space is obtained and preprocessed by at least one of marking a region-of-interest (ROI) in the raw digital representation and classifying the indoor space into a space-category to obtain a preprocessed representation. Using the preprocessed representation, a skeletal map is generated including indicators to identify elements of the indoor space. Revision inputs are received to modify an erroneous indicator from amongst the multiple indicators. The erroneous indicator is revised to a modified indicator and the modified indicator and unmodified indicators are used to create a modified skeletal map which is usable for localizing a mobile device in the indoor space.

Abstract: A method and a device for criterion-based calibration for localization of mobile devices are described. In an example, sensorial data for a zone in the indoor space is obtained. The zone can have a set of factors influencing a behavior of the sensorial data therein. A component of instantaneous localization in the zone is determined based on the sensorial data by simulating variation of the component. The estimation is evaluated based on a ground truth value of the component and, on the basis thereof, a localization criterion is associated with the zone. The localization criterion is indicative of a selectivity in use of the sensorial data for localizing the mobile device in the zone.

Abstract: A method and a device for performing automated calibration are described. In an example, sensorial data gathered for a space to be mapped in an indoor area is obtained from one or more data recording devices. From the sensorial data, a plurality of trajectories are derived which indicate movement of the one or more data recording devices in the space. Based on the plurality of trajectories, a structural map representing a plurality of map constraints of the space is created. Further, from the sensorial data, calibration information associated with a series of locations in the space is extracted and used along with the structural map to generate a fingerprint map of the space.

Abstract: A method and a device for performing targeted calibration are described. In an example, instructions to acquire spatial data in a predefined region in an indoor space is are received, the indoor space divided into a grid of a plurality of polygonal tiles in a size-wise hierarchy and tessellated at each level of the size-wise hierarchy, wherein the instructions comprise information regarding a group of polygonal tiles from amongst the plurality of polygonal tiles in the predefined region to gather the spatial data for, the group of polygonal tiles selected at a hierarchical level in the size-wise hierarchy, and wherein the spatial data is to be gathered for the group of polygonal tiles along a substantially continuous path connecting the group of polygonal tiles to be traversed for gathering the spatial data. In response to the instructions, acquisition of the spatial data along the substantially continuous path may be commenced.

Abstract: A method and a device for performing targeted calibration are described. In an example, a first set of spatial data acquired within a predefined region in an indoor space is received. The indoor space is represented as a grid of tessellated polygonal tiles arranged in a hierarchical structure of size-wise arranged polygonal tiles. a preliminary discretized spatial map of the calibrated spatial data in the predefined region is generated by linking the first set of spatial data and the corresponding polygonal tiles for which the spatial data has been acquired. From the preliminary discretized spatial map, a group of polygonal tiles are identified which have density of calibrated spatial data less than a threshold density. For the identified polygonal tiles, a second set of spatial data may be acquired. Based on the preliminary discretized spatial map and the second set of spatial data, a fingerprint map is generated for the indoor space.

Abstract: A method and a device for localization using grid-based localization of map constraints are described. In an example, an indoor space is divided into a grid of tessellated polygonal tiles in a hierarchical structure. The grid is correlated with physical entities in the indoor space by associating each physical entity with a polygonal tile. Further, an absolute probability value indicative of presence of a device therein is associated with each polygonal tile. As part of associating, the absolute probability value is allocated to each polygonal tile in each hierarchical level to create a probability map for the indoor space. The probability map is used to generate a grid map for the indoor space and the grid map is usable to determine location of the device in the indoor space.

Abstract: Examples for localization using tessellated grids are described. In an example, a plurality of physical entities can be identified in an indoor space and each physical entity can be georeferenced to a coordinate system. The indoor space can be divided into a grid of polygonal tiles abutting adjacent polygonal tiles, and arranged in a hierarchical structure with the tiles in one level of the hierarchical structure being substantially representable by polygonal tiles in other levels of the hierarchical structure. The grid is then spatially indexed by associating each physical entity with a polygonal tile from amongst the polygonal tiles in each hierarchical level, thereby correlating a georeference of the physical entity with the polygonal tile. Based on the spatial indexing, a grid map for the indoor space can be generated for localizing mobile devices in the indoor space.

Abstract: A method and a device for performing targeted calibration are described. In an example, instructions to acquire spatial data in a predefined region in an indoor space is are received, the indoor space divided into a grid of a plurality of polygonal tiles in a size-wise hierarchy and tessellated at each level of the size-wise hierarchy, wherein the instructions comprise information regarding a group of polygonal tiles from amongst the plurality of polygonal tiles in the predefined region to gather the spatial data for, the group of polygonal tiles selected at a hierarchical level in the size-wise hierarchy, and wherein the spatial data is to be gathered for the group of polygonal tiles along a substantially continuous path connecting the group of polygonal tiles to be traversed for gathering the spatial data. In response to the instructions, acquisition of the spatial data along the substantially continuous path may be commenced.

Abstract: A method and a device for performing targeted calibration are described. In an example, a first set of spatial data acquired within a predefined region in an indoor space is received. The indoor space is represented as a grid of tessellated polygonal tiles arranged in a hierarchical structure of size-wise arranged polygonal tiles. a preliminary discretized spatial map of the calibrated spatial data in the predefined region is generated by linking the first set of spatial data and the corresponding polygonal tiles for which the spatial data has been acquired. From the preliminary discretized spatial map, a group of polygonal tiles are identified which have density of calibrated spatial data less than a threshold density. For the identified polygonal tiles, a second set of spatial data may be acquired. Based on the preliminary discretized spatial map and the second set of spatial data, a fingerprint map is generated for the indoor space.