Augmented Reality without Borders: Achieving Precise Localization Without Maps (2408.17373v4)

Abstract: Visual localization is crucial for Computer Vision and Augmented Reality (AR) applications, where determining the camera or device's position and orientation is essential to accurately interact with the physical environment. Traditional methods rely on detailed 3D maps constructed using Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM), which is computationally expensive and impractical for dynamic or large-scale environments. We introduce MARLoc, a novel localization framework for AR applications that uses known relative transformations within image sequences to perform intra-sequence triangulation, generating 3D-2D correspondences for pose estimation and refinement. MARLoc eliminates the need for pre-built SfM maps, providing accurate and efficient localization suitable for dynamic outdoor environments. Evaluation with benchmark datasets and real-world experiments demonstrates MARLoc's state-of-the-art performance and robustness. By integrating MARLoc into an AR device, we highlight its capability to achieve precise localization in real-world outdoor scenarios, showcasing its practical effectiveness and potential to enhance visual localization in AR applications.

• The paper presents a novel map-free AR localization framework that triangulates keypoints from sequential images, eliminating the need for pre-built maps.
• It leverages robust image retrieval and matching techniques such as NetVLAD, APGeM, SuperPoint, and SuperGlue for effective candidate selection.
• Experiments on Niantic and LaMAR datasets demonstrate state-of-the-art performance with a median APE of 5.55 cm and 0.80°, while significantly reducing computational overhead.

Precise Localization in AR Without Pre-Built Maps

The paper "Augmented Reality without Borders: Achieving Precise Localization Without Maps" presents a novel framework for visual localization tailored to Augmented Reality (AR) applications. Written by Albert Gassol Puigjaner, Irvin Aloise, and Patrik Schmuck from Magic Leap, the paper introduces a method to achieve precise localization without relying on pre-built 3D maps, suggesting significant advancements in computational efficiency and practicality for dynamic and large-scale environments.

Introduction

The conventional approaches to Visual Localization in AR hinge on complex and resource-intensive processes like Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM). These methods construct detailed 3D maps of the environment, which, while effective, are computationally expensive and impractical for dynamic or large-scale settings. This paper proposes an alternative method that leverages known relative transformations within image sequences to perform intra-sequence triangulation, effectively eliminating the need for pre-generated maps.

Methodology

The proposed framework, termed ** (Map-free Augmented Reality Localization), leverages a sequence of query images to create a local representation of the 3D structure around the user's position. This local representation is then used to estimate the relative pose to a given world-registered reference image. The key components of the method include:

1. Candidate Selection and Matching: Utilizes NetVLAD and APGeM descriptors for image retrieval and SuperPoint and SuperGlue for local feature extraction and matching.
2. Intra-sequence Query Keypoints Triangulation: Employs known relative transformations between frames to triangulate 2D keypoints into 3D space without prior geometry.
3. Pose Estimation and Pose Graph Optimization (PGO): Implements standard Perspective-n-Point (PnP) algorithms for initial pose estimation, followed by a non-linear refinement using pose graph optimization to enhance accuracy.

Experimental Evaluation

The paper evaluates the performance of ** on the Niantic Map-Free Relocalization Dataset and the LaMAR dataset. The Niantic dataset, which is a standard benchmark for map-free localization, demonstrates that ** achieves state-of-the-art (SOTA) performance with significantly lower median Absolute Pose Error (APE) compared to other methods, such as MicKey and Relative Pose Regression (RPR). The evaluation on the LaMAR dataset, which includes both indoor and outdoor AR scenes, reveals that ** achieves competitive accuracy compared to traditional SfM-based methods like LaMAR but without the need for pre-built maps.

Results

** achieved a median APE of 5.55 cm and 0.80° in the Niantic dataset, outperforming competitors and demonstrating its robust performance. On the LaMAR dataset, ** showcased competitive performance with median APE values within the same range as traditional methods, yet with significantly reduced computational overhead. The paper also highlights the effectiveness of ** in practical scenarios through real-world experiments using a Magic Leap 2 (ML2) device and publicly available map data from Mapillary.

Implications and Future Work

The implications of this research are substantial for both theoretical and practical applications in AR and Computer Vision. By eliminating the necessity for pre-built maps, ** offers a more efficient and scalable solution for AR localization, particularly beneficial in dynamically changing or large-scale environments. Furthermore, the framework's integration into a commercially available AR headset underscores its practical viability.

Future developments could explore further optimization of the triangulation and pose estimation processes, integration with other sensor modalities such as LiDAR, and extending the applicability of ** to a broader range of environments and use-cases. Additionally, incorporating learning-based methods for enhancing feature matching robustness in varying environmental conditions could further augment the framework's versatility and reliability.

Conclusion

The paper presents a notable advancement in AR localization by introducing the ** framework, which achieves precise localization without the computational burden of pre-built maps. Through a combination of innovative image matching, triangulation, and pose refinement techniques, ** demonstrates state-of-the-art performance and practical applicability, potentially setting a new standard for AR localization methods. The research paves the way for more efficient and scalable AR applications, addressing critical limitations of existing methods and opening avenues for future innovations in the field.