Artificial Reality Scene Realignment for Localization Failure Recovery
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Solution Overview
Problem
Existing artificial reality systems face challenges in maintaining a persistent and up-to-date representation of a user's physical environment, leading to localization failures when relocalizing scene models, which requires users to redo the scene capture process, causing frustration and inefficiency.
Innovation Solution
A scene model is generated using a guided scene capture workflow, allowing users to easily capture and query their environment, and a scene realignment solution that aligns a previously saved room model with the current map based on user input to mitigate localization failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a scene model is captured and stored for later use, then the user experience is improved by allowing quick recovery of virtual environments, but localization failures occur when relocalizing the scene model, requiring users to redo the entire capture process
Solution Approach 1:
The system performs preliminary scene capture and stores the scene model with associated metadata (images, depth maps, semantic information) in advance. This preliminary action creates a reusable representation that can be quickly recovered without repeating the entire capture process, while the stored metadata serves as reference data for subsequent localization attempts.
Solution Approach 2:
The system introduces an intermediary relocalization process that uses extracted features from the stored scene model (such as keypoint descriptors, semantic anchors, or distinctive visual markers) to match against current sensor data. This intermediary mechanism bridges the gap between stored historical data and current environmental sensing, enabling reliable relocalization without full recapture.
2Reliability
If the system requires users to redo the scene capture process after localization failure, then localization accuracy may be restored, but user frustration increases and efficiency decreases
Solution Approach 1:
The system implements self-service relocalization by automatically attempting to recover the scene model using stored metadata and feature matching algorithms. Instead of requiring user intervention to recapture the environment, the system autonomously performs feature extraction, comparison, and pose estimation to restore localization, thereby maintaining reliability while preserving user convenience.
Solution Approach 2:
The scene capture process performs preliminary actions of storing not only the geometric structure but also rich metadata including multiple view images, depth information, semantic segmentations, and extracted keypoint descriptors. This preliminary enrichment of stored data enables automatic relocalization attempts without requiring users to repeat the capture process.
3Measurement precision
If the system stores detailed scene models for relocalization, then relocalization accuracy improves, but system complexity and processing requirements increase
Solution Approach 1:
The system extracts only the essential and most discriminative features from the complete scene model for storage and relocalization purposes. Instead of storing and processing all raw sensor data, it extracts keypoint descriptors, semantic anchors, or distinctive visual markers that capture the essential geometric and semantic characteristics needed for accurate relocalization, thereby reducing complexity while maintaining precision.
Solution Approach 2:
The scene model is segmented into hierarchical levels of detail, with only the most critical components (such as distinctive keypoints, semantic boundaries, or anchor points) being stored for relocalization. This segmentation allows the system to maintain high relocalization precision using a subset of features rather than the complete scene representation, reducing processing requirements and system complexity.
Data Source
AI summary
In particular embodiments, a computing system may initiate a scene alignment process to align a previous map of a scene with a current map of the scene. The system may send instructions to a user wearing an artificial-reality system to select a set of entities in the scene. The system may receive a selection of the set of entities in the scene. The system may determine a particular point in the scene based on an intersection of selected set of entities. The system may align the previous map with the current map based on the particular point in the scene. The system may load a scene model associated with the previous map into the current map.