Gesture sequence recognition using simultaneous localization and mapping (SLAM) components in virtual, augmented, and mixed reality (xR) applications

#The present disclosure generally relates to Information Handling Systems (IHSs), and, more particularly, to systems and methods for gesture sequence recognition using Simultaneous Localization and Mapping (SLAM) components in virtual, augmented, and mixed reality (xR) applications.

#As the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option available to users is Information Handling Systems (IHSs). An IHS generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information. Because technology and information handling needs and requirements vary between different users or applications, IHSs may also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information may be processed, stored, or communicated. The variations in IHSs allow for IHSs to be general or configured for a specific user or specific use such as financial transaction processing, airline reservations, enterprise data storage, or global communications. In addition, IHSs may include a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems, data storage systems, and networking systems.

#IHSs may be used to produce virtual, augmented, or mixed reality (xR) applications. The goal of virtual reality (VR) is to immerse users in virtual environments. A conventional VR device obscures a user's real-world surroundings, such that only digitally-generated images remain visible. In contrast, augmented reality (AR) and mixed reality (MR) operate by overlaying digitally-generated content or entities (e.g., characters, text, hyperlinks, images, graphics, etc.) upon the user's real-world, physical surroundings. A typical AR/MR device includes a projection-based optical system that displays content on a translucent or transparent surface of an HMD, heads-up display (HUD), eyeglasses, or the like (collectively “HMDs”).

#The present invention(s) is/are illustrated by way of example and is/are not limited by the accompanying figures. Elements in the figures are illustrated for simplicity and clarity, and have not necessarily been drawn to scale.

#FIG. 1 is a perspective view of an example of an environment where a virtual, augmented, or mixed reality (xR) application may be executed, according to some embodiments.

#FIG. 2 is a block diagram of an example of a Head-Mounted Device (HMD) and a host Information Handling System (IHS), according to some embodiments.

#Embodiments described herein provide systems and methods for gesture sequence recognition using Simultaneous Localization and Mapping (SLAM) components in virtual, augmented, and mixed reality (xR) applications. These techniques are particularly useful in xR applications that employ HMDs, Heads-Up Displays (HUDs), and eyeglasses—collectively referred to as “HMDs.”

#As used herein, the term SLAM refers systems and methods that use positional tracking devices to construct a map of an unknown environment where an HMD is located, and that simultaneously identifies where the HMD is located, its orientation, and/or pose.

#Generally, SLAM methods implemented in connection with xR applications may include a propagation component, a feature extraction component, a mapping component, and an update component. The propagation component may receive angular velocity and accelerometer data from an Inertial Measurement Unit (IMU) built into the HMD, for example, and it may use that data to produce a new HMD position and/or pose estimation. A camera (e.g., a depth-sensing camera) may provide video frames to the feature extraction component, which extracts useful image features (e.g., using thresholding, blob extraction, template matching, etc.), and generates a descriptor for each feature. These features, also referred to as “landmarks,” are then fed to the mapping component.