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: 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: 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: According to various embodiments, a user interface (UI) includes a precedence graph area and an icon list displaying multiple types of program icons. A user selection of one of the program icons is received, the user selection corresponding to moving the selected program icon to the precedence graph area, the selected program icon referencing a composer UI to generate content of a specific media type. The selected program icon is characterized as a first program-content-pairing icon that references the content created by the composer UI. Thereafter, a user interaction with a plurality of program-content-pairing icons in the precedence graph area is detected, the user interaction corresponding to specifying an ordering of the plurality of program-content-pairing icons. A program flow precedence graph referencing a program flow of an interactive program is the generated, based on the ordering of the program-content-pairing icons in the precedence graph area.

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: According to various embodiments, a user interface (UI) includes a precedence graph area and an icon list displaying multiple types of program icons. A user selection of one of the program icons is received, the user selection corresponding to moving the selected program icon to the precedence graph area, the selected program icon referencing a composer UI to generate content of a specific media type. The selected program icon is characterized as a first program-content-pairing icon that references the content created by the composer UI. Thereafter, a user interaction with a plurality of program-content-pairing icons in the precedence graph area is detected, the user interaction corresponding to specifying an ordering of the plurality of program-content-pairing icons. A program flow precedence graph referencing a program flow of an interactive program is the generated, based on the ordering of the program-content-pairing icons in the precedence graph area.

Abstract: According to various embodiments, a user interface (UI) includes a precedence graph area and an icon list displaying multiple types of program icons. A user selection of one of the program icons is received, the user selection corresponding to moving the selected program icon to the precedence graph area, the selected program icon referencing a composer UI to generate content of a specific media type. The selected program icon is characterized as a first program-content-pairing icon that references the content created by the composer UI. Thereafter, a user interaction with a plurality of program-content-pairing icons in the precedence graph area is detected, the user interaction corresponding to specifying an ordering of the plurality of program-content-pairing icons. A program flow precedence graph referencing a program flow of an interactive program is the generated, based on the ordering of the program-content-pairing icons in the precedence graph area.