Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth Knowledge (2408.13085v3)

Abstract: Map-free relocalization technology is crucial for applications in autonomous navigation and augmented reality, but relying on pre-built maps is often impractical. It faces significant challenges due to limitations in matching methods and the inherent lack of scale in monocular images. These issues lead to substantial rotational and metric errors and even localization failures in real-world scenarios. Large matching errors significantly impact the overall relocalization process, affecting both rotational and translational accuracy. Due to the inherent limitations of the camera itself, recovering the metric scale from a single image is crucial, as this significantly impacts the translation error. To address these challenges, we propose a map-free relocalization method enhanced by instance knowledge and depth knowledge. By leveraging instance-based matching information to improve global matching results, our method significantly reduces the possibility of mismatching across different objects. The robustness of instance knowledge across the scene helps the feature point matching model focus on relevant regions and enhance matching accuracy. Additionally, we use estimated metric depth from a single image to reduce metric errors and improve scale recovery accuracy. By integrating methods dedicated to mitigating large translational and rotational errors, our approach demonstrates superior performance in map-free relocalization techniques.

• The paper presents a hierarchical matching method that integrates instance segmentation with local feature matching to enhance map-free relocalization accuracy.
• The paper demonstrates that integrating depth estimation effectively recovers translation scale, reducing median translation error significantly.
• The paper validates the approach on challenging datasets, showing improved generalization and robustness for autonomous navigation and AR applications.

Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth Knowledge

The paper "Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth Knowledge" presents a novel approach to visual relocalization, addressing the significant challenges inherent in map-free scenarios. Traditional visual relocalization methods rely on pre-built 3D maps, which are often impractical due to memory limitations and the extensive resources required to create and maintain these maps. This paper proposes a framework that leverages instance knowledge and depth estimation to enhance the accuracy of map-free visual localization, a critical advancement for applications in autonomous navigation and augmented reality.

Contributions and Methodology

The paper introduces a comprehensive framework that employs instance segmentation and depth estimation to tackle the key challenges of visual relocalization without pre-built maps. The primary contributions of this work are as follows:

1. Hierarchical Matching Method: The proposed system integrates instance-level and feature-level matching. This method combines global instance matching with local feature matching, significantly improving relocalization accuracy in map-free environments.
2. Instance Knowledge: By using instance segmentation to extract main objects within the scene, the feature point matching process is refined to focus on meaningful regions, enhancing matching precision and reducing errors due to incorrect matches across different objects.
3. Depth Knowledge: The framework incorporates a sophisticated depth estimation technique to project 2D features into 3D space accurately. This integration allows for reliable recovery of the translation vector scale, which is crucial for minimizing translation errors.

Experimental Validation

The proposed method was validated against the MapFree-Reloc dataset, which includes challenging scenarios such as dynamic environments, significant viewpoint shifts, and minimal visual overlap between reference and query images. The results demonstrated that the method significantly outperforms existing approaches in several key metrics:

1. Average Median Pose Error: The proposed method achieved an average median translation error of 0.596 meters and an average median rotation error of 9.030 degrees, which represents a significant improvement over existing methods, such as RPR [3D-3D], which recorded errors of 1.667 meters and 22.623 degrees, respectively.
2. AUC@VCRE < 90px: The method achieved an AUC of 0.849, significantly higher than competing methods.
3. Generalization and Robustness: The method was evaluated on complex scenarios within the Map-free dataset, including spatiotemporal variations and significant parallax, where it demonstrated superior generalization performance.

Comparative Analysis

The paper provides a detailed comparative analysis with other state-of-the-art methods. The comparison includes both component-wise methods (e.g., SIFT, LoFTR, SuperGlue with different depth and pose estimation techniques) and end-to-end approaches (e.g., RPR variants). The experimental results clearly indicate the superiority of the proposed method in terms of both rotational and translational accuracy.

Implications and Future Directions

The proposed relocalization method has significant practical implications for autonomous navigation and augmented reality, where reliable localization without pre-built maps is essential. The integration of instance segmentation and depth estimation into the relocalization process represents a substantial advancement in reducing both rotational and translation errors.

Future research could explore:

• Improvement of Instance Segmentation: Enhancing instance segmentation algorithms to better handle occlusions and dynamic scene changes.
• Advanced Depth Estimation Techniques: Incorporating real-time depth estimation methods to further improve localization accuracy.
• Scalability: Extending the approach to handle large-scale environments and assessing its performance in diverse real-world scenarios.

Conclusion

This paper introduces a robust and accurate map-free visual relocalization method by integrating instance knowledge and depth knowledge. The proposed hierarchical matching methodology demonstrates significant improvements over existing approaches, reducing both rotational and translation errors. The practical implications of this method are profound, offering a promising direction for future advancements in autonomous navigation and augmented reality.