Technology for providing spatial positioning for virtual world of meta universe based on RTK and Slam

Abstract

The invention provides an RTK and Slam-based space positioning technology for a meta-universe virtual world. The system comprises the following functional modules: an RTK positioning module, which is used for obtaining a GNSS signal and obtaining an absolute position through differential correction; the SLAM sensing module is used for constructing an environment feature map through vision, a laser radar or an IMU (Inertial Measurement Unit) and outputting a relative pose; the fusion calculation module is used for fusing the absolute position and the relative pose by adopting extended Kalman filtering or factor graph optimization, and outputting a corrected fusion pose; and the VR / MR interface module is used for transmitting the fusion pose to a VR / MR system so as to realize real-time rendering and multi-user synchronous positioning. Through multi-source sensor fusion and error correction, centimeter-level precision absolute positioning, low-delay real-time performance, robustness in signal shielding and complex environments and multi-user coordinate system sharing are achieved, and outdoor VR / MR immersion interaction is supported.

Description

Technical Field

[0001] This invention relates to the field of virtual reality and mixed reality technology, specifically to providing spatial positioning technology for the metaverse virtual world based on RTK and SLAM. Background Technology

[0002] With the development of VR / MR technology, an increasing number of cultural tourism, competitive, educational, and entertainment scenarios require centimeter-level, low-latency, and continuously stable spatial positioning capabilities for users in large-scale outdoor environments. However, existing technologies have the following shortcomings:

[0003] Limitations of Real-Time Kinematic (RTK): RTK relies on GNSS signals and differential base stations to provide centimeter-level accuracy, but in multipath interference environments such as urban canyons and tree obstructions, signal interruption can lead to a decrease or even loss of positioning accuracy.

[0004] Limitations of Simultaneous Localization and Mapping (SLAM): SLAM relies on vision, radar, or IMU to build environmental maps. Its localization results are relative coordinates, which are prone to cumulative errors and cannot provide a global absolute coordinate system.

[0005] There is a lack of suitable fusion positioning solutions for VR / MR: VR / MR applications are latency-sensitive (typically requiring less than 50ms) and require multiple users to share a global coordinate system. Existing positioning technologies struggle to balance high accuracy, low latency, and robustness. Therefore, this invention provides a spatial positioning technology for the metaverse virtual world based on RTK and SLAM. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this invention is to provide a spatial positioning technology for the metaverse virtual world based on RTK and SLAM, thereby solving the problems mentioned in the background. This invention achieves centimeter-level absolute positioning accuracy, low latency (<50ms) real-time performance, robustness in signal obstruction and complex environments, and multi-user shared coordinate system through multi-source sensor fusion and error correction, and supports outdoor VR / MR immersive interaction.

[0007] To achieve the above objectives, the present invention provides spatial positioning technology for the metaverse virtual world based on RTK and SLAM, including a spatial positioning system comprising the following functional modules:

[0008] The RTK positioning module is used to acquire GNSS signals and obtain absolute position through differential correction.

[0009] The SLAM perception module is used to construct an environmental feature map through vision, lidar, or IMU and output the relative pose.

[0010] The fusion calculation module is used to fuse the absolute position and the relative pose using extended Kalman filtering or factor graph optimization, and output the corrected fused pose.

[0011] The VR / MR interface module is used to transmit the fused pose to the VR / MR system to achieve real-time rendering and multi-user synchronous positioning.

[0012] Furthermore, the RTK positioning module includes a GNSS receiver to acquire high-precision satellite positioning data, and outputs centimeter-level absolute coordinates through differential correction by an RTK base station or network.

[0013] Furthermore, the SLAM perception module includes a visual camera, a lidar, and an IMU sensor, used to acquire environmental feature point information, construct a local map, and estimate relative pose.

[0014] Furthermore, the fusion computing module includes an edge computing unit that uses extended Kalman filtering (EKF) or factor graph optimization to perform drift correction on the SLAM output, taking the absolute position of the RTK as a global constraint.

[0015] Furthermore, the fusion computing module supports dynamic weight adjustment to handle situations where RTK signals are weak or SLAM accumulated errors occur.

[0016] Furthermore, the VR / MR interface module is used to transmit the fused pose data to the VR / MR rendering engine.

[0017] Furthermore, it also includes the spatial positioning fusion algorithm process:

[0018] S1: Receive RTK differential positioning data and obtain preliminary absolute coordinates;

[0019] S2: The SLAM module constructs a local map based on environmental features and outputs the relative pose;

[0020] S3: The fusion module optimizes the fusion of RTK and SLAM data based on EKF / factor graph, dynamically adjusts weights, and eliminates drift;

[0021] S4: Outputs fused high-precision pose data to the VR / MR system, supporting multi-user synchronization.

[0022] Furthermore, adaptive weight adjustment during dynamic occlusion: weighted SLAM is applied when the RTK signal is weak; RTK correction is used when the SLAM error increases.

[0023] Furthermore, the fusion algorithm provides edge / cloud deployment to reduce latency and allows multiple users to share the same global coordinates.

[0024] Furthermore, the fusion algorithm is an extended Kalman filter or factor graph optimization, and the fusion weights are adaptively adjusted in a dynamic occlusion environment.

[0025] The beneficial effects of this invention are:

[0026] 1. This RTK and SLAM-based spatial positioning technology provides spatial positioning for the metaverse virtual world, achieving centimeter-level high accuracy in a wide range of outdoor environments. It can ensure low latency, meet the needs of VR / MR interaction, maintain robustness even in environments with signal interference, and support simultaneous access by multiple users, meeting the needs of large-scale outdoor immersive applications. Attached Figure Description

[0027] Figure 1 This is a system architecture diagram of the present invention, which provides spatial positioning technology for the metaverse virtual world based on RTK and SLAM.

[0028] Figure 2 This is a flowchart illustrating the algorithm for providing spatial positioning technology to the metaverse virtual world based on RTK and SLAM, as described in this invention. Detailed Implementation

[0029] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0030] Please see Figures 1 to 2 This invention provides the following technical solution: providing spatial positioning technology for the metaverse virtual world based on RTK and SLAM, including a spatial positioning system, which comprises the following functional modules:

[0031] The RTK positioning module is used to acquire GNSS signals and obtain absolute position through differential correction.

[0032] The SLAM perception module is used to construct an environmental feature map through vision, lidar, or IMU and output the relative pose.

[0033] The fusion calculation module is used to fuse the absolute position and the relative pose using extended Kalman filtering or factor graph optimization, and output the corrected fused pose.

[0034] The VR / MR interface module is used to transmit the fused pose to the VR / MR system to achieve real-time rendering and multi-user synchronous positioning.

[0035] This spatial positioning technology achieves centimeter-level high accuracy in a wide range of outdoor environments, ensuring low latency to meet the needs of VR / MR interaction. Furthermore, it maintains robustness even in environments with signal interference and supports simultaneous access by multiple users, meeting the requirements for large-scale outdoor immersive applications.

[0036] In this embodiment, the RTK positioning module includes a GNSS receiver, which acquires high-precision satellite positioning data and outputs centimeter-level absolute coordinates through differential correction by an RTK base station or network.

[0037] In this embodiment, the SLAM perception module includes a visual camera, a lidar, and an IMU sensor, used to acquire environmental feature point information, construct a local map, and estimate relative pose.

[0038] In this embodiment, the fusion computing module includes an edge computing unit, which uses extended Kalman filtering (EKF) or factor graph optimization to perform drift correction on the SLAM output by taking the absolute position of RTK as a global constraint.

[0039] In this embodiment, the fusion computing module supports dynamic weight adjustment to handle situations where RTK signals are weak or SLAM accumulated errors occur.

[0040] In this embodiment, the VR / MR interface module is used to transmit the fused pose data to the VR / MR rendering engine.

[0041] This embodiment also includes a spatial positioning fusion algorithm process:

[0042] S1: Receive RTK differential positioning data and obtain preliminary absolute coordinates;

[0043] S2: The SLAM module constructs a local map based on environmental features and outputs the relative pose;

[0044] S3: The fusion module optimizes the fusion of RTK and SLAM data based on EKF / factor graph, dynamically adjusts weights, and eliminates drift;

[0045] S4: Outputs fused high-precision pose data to the VR / MR system, supporting multi-user synchronization.

[0046] In this embodiment, adaptive weight adjustment is performed during dynamic occlusion: weighted SLAM is applied when the RTK signal is weak; RTK correction is used when the SLAM error increases.

[0047] In this embodiment, the fusion algorithm is provided for edge / cloud deployment to reduce latency and allow multiple users to share the same global coordinates.

[0048] In this embodiment, the fusion algorithm is an extended Kalman filter or factor graph optimization, and the fusion weights are adaptively adjusted under dynamic occlusion conditions.

[0049] Based on the above technical solutions, this embodiment also provides the following specific application scenarios:

[0050] 1. Immersive experiences at tourist attractions:

[0051] RTK base stations are deployed in scenic areas, and tourists wear MR headsets. The devices integrate GNSS, cameras, and IMUs. The high-precision location is obtained through the fusion algorithm of this invention, and the tour guide content is rendered.

[0052] 2. Outdoor sporting events:

[0053] Athletes and spectators wear MR devices to obtain their own pose and the positions of other users in real time, enabling interactive AR effects.

[0054] The foregoing has shown and described the basic principles and main features of the present invention and its advantages. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0055] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A spatial positioning technology for the metaverse virtual world based on RTK and SLAM, including a spatial positioning system, characterized in that: The spatial positioning system includes the following functional modules: The RTK positioning module is used to acquire GNSS signals and obtain absolute position through differential correction. The SLAM perception module is used to construct an environmental feature map through vision, lidar, or IMU and output the relative pose. The fusion calculation module is used to fuse the absolute position and the relative pose using extended Kalman filtering or factor graph optimization, and output the corrected fused pose. The VR / MR interface module is used to transmit the fused pose to the VR / MR system to achieve real-time rendering and multi-user synchronous positioning.

2. The spatial positioning technology for the metaverse virtual world based on RTK and SLAM as described in claim 1, characterized in that: The RTK positioning module includes a GNSS receiver, which acquires high-precision satellite positioning data and outputs centimeter-level absolute coordinates through differential correction by an RTK base station or network.

3. The spatial positioning technology for the metaverse virtual world based on RTK and SLAM as described in claim 1, characterized in that: The SLAM perception module includes a visual camera, a lidar, and an IMU sensor, used to acquire environmental feature point information, construct a local map, and estimate relative pose.

4. The spatial positioning technology for the metaverse virtual world based on RTK and SLAM as described in claim 1, characterized in that: The fusion computing module includes an edge computing unit, which uses extended Kalman filtering or factor graph optimization, and uses the absolute position of RTK as a global constraint to perform drift correction on the SLAM output.

5. The spatial positioning technology for the metaverse virtual world based on RTK and SLAM as described in claim 4, characterized in that: The fusion computing module supports dynamic weight adjustment to handle situations where RTK signals are weak or SLAM accumulated errors occur.

6. The spatial positioning technology for the metaverse virtual world based on RTK and SLAM as described in claim 1, characterized in that: The VR / MR interface module is used to transmit the fused pose data to the VR / MR rendering engine.

7. The spatial positioning technology for the metaverse virtual world based on RTK and SLAM as described in claim 1, characterized in that, It also includes the spatial positioning fusion algorithm process: S1: Receive RTK differential positioning data and obtain preliminary absolute coordinates; S2: The SLAM module constructs a local map based on environmental features and outputs the relative pose; S3: The fusion module optimizes the fusion of RTK and SLAM data based on EKF / factor graph, dynamically adjusts weights, and eliminates drift; S4: Outputs fused high-precision pose data to the VR / MR system, supporting multi-user synchronization.

8. The spatial positioning technology for the metaverse virtual world based on RTK and SLAM as described in claim 7, characterized in that, Adaptive weight adjustment during dynamic occlusion: weighted SLAM is applied when the RTK signal is weak; RTK correction is applied when the SLAM error increases.

9. The spatial positioning technology for the metaverse virtual world based on RTK and SLAM as described in claim 7, characterized in that: The fusion algorithm provides edge / cloud deployment to reduce latency and allows multiple users to share the same global coordinates.

10. The spatial positioning technology for the metaverse virtual world based on RTK and SLAM as described in claim 9, characterized in that: The fusion algorithm is an extended Kalman filter or factor graph optimization, and the fusion weights are adaptively adjusted under dynamic occlusion conditions.

Images