Abstract: In an embodiment of the present invention, a GraphSLAM-like algorithm for signal strength SLAM is presented. This algorithm as an embodiment of the present invention shares many of the benefits of Gaussian processes yet is viable for a broader range of environments since it makes no signature uniqueness assumptions. It is also more tractable to larger map sizes, requiring O(N2) operations per iteration. In the present disclosure, an algorithm according to an embodiment of the present invention is compared to a laser-SLAM ground truth, showing that is produces excellent results in practice.

Abstract: Systems and methods for providing a visual scroll representation of production data on a display receive a digital script comprising production data, wherein the production data comprises a plurality of production elements; extract the plurality of production elements from the digital script; classify each of the plurality of production elements into one or more predefined classes; map at least one of the one or more predefined classes to one or more relations, wherein a relation represents a definable attribute of at least two predefined classes; determine a respective weight for each of the one or more relations; generate the visual scroll representation of the plurality of production elements, wherein visual representation of each of the one or more production elements is based at least in part on the determined weight of each of the one or more relations; and display the visual scroll representation on the display.

Abstract: In an embodiment of the present invention, a GraphSLAM-like algorithm for signal strength SLAM is presented. This algorithm as an embodiment of the present invention shares many of the benefits of Gaussian processes yet is viable for a broader range of environments since it makes no signature uniqueness assumptions. It is also more tractable to larger map sizes, requiring O(N2) operations per iteration. In the present disclosure, an algorithm according to an embodiment of the present invention is compared to a laser-SLAM ground truth, showing that is produces excellent results in practice.

Abstract: Systems and methods for providing a visual scroll representation of production data on a display receive a digital script comprising production data, wherein the production data comprises a plurality of production elements; extract the plurality of production elements from the digital script; classify each of the plurality of production elements into one or more predefined classes; map at least one of the one or more predefined classes to one or more relations, wherein a relation represents a definable attribute of at least two predefined classes; determine a respective weight for each of the one or more relations; generate the visual scroll representation of the plurality of production elements, wherein visual representation of each of the one or more production elements is based at least in part on the determined weight of each of the one or more relations; and display the visual scroll representation on the display.

Abstract: In an embodiment of the present invention, a GraphSLAM-like algorithm for signal strength SLAM is presented. This algorithm as an embodiment of the present invention shares many of the benefits of Gaussian processes yet is viable for a broader range of environments since it makes no signature uniqueness assumptions. It is also more tractable to larger map sizes, requiring O(N2) operations per iteration. In the present disclosure, an algorithm according to an embodiment of the present invention is compared to a laser-SLAM ground truth, showing that it produces excellent results in practice.

Abstract: In an embodiment of the present invention, a GraphSLAM-like algorithm for signal strength SLAM is presented. This algorithm as an embodiment of the present invention shares many of the benefits of Gaussian processes yet is viable for a broader range of environments since it makes no signature uniqueness assumptions. It is also more tractable to larger map sizes, requiring O(N2) operations per iteration. In the present disclosure, an algorithm according to an embodiment of the present invention is compared to a laser-SLAM ground truth, showing that it produces excellent results in practice.

Abstract: In an embodiment of the present invention, a GraphSLAM-like algorithm for signal strength SLAM is presented. This algorithm as an embodiment of the present invention shares many of the benefits of Gaussian processes yet is viable for a broader range of environments since it makes no signature uniqueness assumptions. It is also more tractable to larger map sizes, requiring O(N2) operations per iteration. In the present disclosure, an algorithm according to an embodiment of the present invention is compared to a laser-SLAM ground truth, showing that it produces excellent results in practice.