XR Pose Tracking with Stable Key Frames and Map Reuse
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Solution Overview
Problem
Current pose tracking methods in extended reality (XR) systems face inefficiencies such as waiting for map transmission, outdated map data, large pose drift, and long map loading times due to map data synchronization issues between head-mounted displays (HMDs) and portable devices.
Innovation Solution
A method for pose tracking that includes receiving a stable key frame from an HMD, determining its availability based on time duration, power status, and map identification, and updating the map using SLAM algorithms for efficient relocalization, reducing pose drift and map loading time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the tracker waits for map transmission to complete before reusing the map for tracking, then the map data is synchronized with the HMD, but the tracking time is delayed and efficiency is reduced
Solution Approach 1:
The system performs preliminary actions by having the HMD continuously capture and store key frames in advance, and the tracker pre-processes and stores map data during periods when it is not actively tracking. This allows the tracker to have map data ready before it is needed, eliminating the need to wait for transmission during critical tracking moments.
Solution Approach 2:
The map data is segmented into key frames that can be independently processed and transmitted. The HMD captures a sequence of key frames, and the tracker can selectively receive and use these segmented frames for tracking, rather than waiting for complete map transmissions.
2Productivity
If the tracker reuses the saved map for tracking, then the tracking efficiency is improved, but the map data becomes outdated when the HMD creates a new map
Solution Approach 1:
The system implements feedback mechanisms where the tracker monitors the availability and currency of its map data by checking key frame availability indicators from the HMD. When the tracker detects that its saved map data is outdated or key frames are unavailable, it automatically initiates a map update by requesting new map data from the HMD, thus maintaining both efficiency and currency.
3Reliability
If lots of outdated map data are removed quickly from the tracker, then the map synchronization is improved, but the tracking is hindered
Solution Approach 1:
The tracker performs preliminary actions by pre-processing and storing map data during idle periods before it is needed for tracking. This allows the system to have updated map data ready in advance, so when synchronization is needed, the tracker can switch to pre-prepared data without interrupting the tracking process.
4Area of stationary object
If the tracker loads a large map, then the map coverage is improved, but the loading time is extended
Solution Approach 1:
The large map is segmented into multiple key frames that are captured sequentially by the HMD. The tracker can load and process these segmented key frames incrementally during idle periods, rather than attempting to load the entire large map at once, thus reducing the peak loading time while maintaining comprehensive coverage.
Solution Approach 2:
The HMD captures and prepares key frames in advance during periods when the tracker is not actively tracking. This preliminary action allows the tracker to have map data ready before it is needed, reducing the effective loading time during active tracking sessions.
5Productivity
If the tracker uses the saved map in stable environments, then the tracking efficiency is improved, but the pose drift increases when the environment changes
Solution Approach 1:
The tracker implements feedback by monitoring key frame availability indicators from the HMD and comparing them with its local map data. When environmental changes cause discrepancies or key frames become unavailable, the system detects this through the feedback mechanism and triggers a map update, thereby maintaining pose accuracy while preserving the efficiency benefits of map reuse in stable environments.
Data Source
AI summary
A method of pose tracking, a method of generating a stable key frame for pose tracking, a portable device, and a head-mounted display are provided. The method of pose tracking includes: receiving a stable key frame from the head-mounted display; receiving map data from the head-mounted display; determining whether the stable key frame is available according to information, wherein the information is obtained from the head-mounted display; and in response to the stable key frame being available, updating a map for the pose tracking according to the stable key frame and the map data.


