Improving Camera Relocalisation Accuracy and Efficiency through Uncertainty-Aware Pose Estimation and Refinement

Основні поняття

This work introduces a novel APR-agnostic framework, HR-APR, that formulates uncertainty estimation as cosine similarity estimation between the query and database features. It leverages the uncertainty information to enhance the performance of APR through an iterative pose refinement pipeline.

Анотація

The paper proposes a novel APR-agnostic framework, HR-APR, that addresses the limitations of existing Absolute Pose Regressor (APR) methods. Key highlights:

1. Uncertainty Estimation Module:

   • Integrates a new pose-based retrieval algorithm to fetch image feature embeddings from the training set.
   • Calculates the cosine similarity between the query image features and the retrieved features to measure the uncertainty of the APR output.
   • This uncertainty-aware approach is flexible and computationally efficient, as it does not rely on or affect the underlying APR network architecture.

2. Pose Refinement Module:

   • Leverages the uncertainty information from the estimation module to optimize an iterative pose refinement pipeline.
   • Refines high-similarity poses with fewer steps and low-similarity poses with more steps, reducing the overall computational overhead.

3. Extensive Experiments:

   • Evaluates the effectiveness of the uncertainty estimation module across three different APR models on indoor and outdoor datasets.
   • Demonstrates a clear correlation between the predicted uncertainty and the actual pose error.
   • Shows that the proposed framework can reduce the computational overhead of the refinement pipeline by 27.4% and 15.2% on the indoor and outdoor datasets, respectively, while maintaining the state-of-the-art accuracy of single-image APR methods.

The key innovation of this work is the development of an APR-agnostic uncertainty estimation module that can be seamlessly integrated with various APR architectures to improve their robustness and efficiency through uncertainty-aware pose refinement.

Статистика

The training set of the 7Scenes dataset contains 1000 to 7000 images per scene, and the test set contains 1000 to 5000 images per scene.
The Cambridge Landmarks dataset contains 231 to 1487 training images and 103 to 530 test images per scene.

Цитати

"Uncertainty-aware (UA) APRs aim to distinguish unreliable predictions by providing additional uncertainty information with the estimated pose."
"Our framework enables greater flexibility regarding APR architecture and pose refinement compared to existing UA APRs."
"The proposed uncertainty estimation module displays a clear correlation between pose error and uncertainty with similar performance across APR models, demonstrating the validity of the approach."

Ключові висновки, отримані з

HR-APR: APR-agnostic Framework with Uncertainty Estimation and Hierarchical Refinement for Camera Relocalisation

by Changkun Liu... о arxiv.org 04-22-2024

https://arxiv.org/pdf/2402.14371.pdf

Глибші Запити

How can the proposed uncertainty estimation module be extended to handle dynamic environments or scenes with significant changes over time

The proposed uncertainty estimation module can be extended to handle dynamic environments or scenes with significant changes over time by incorporating temporal information into the feature embeddings. One approach could be to include a recurrent neural network (RNN) or a long short-term memory (LSTM) network to capture the temporal dependencies in the image sequences. By considering the evolution of features over time, the uncertainty estimation module can adapt to changes in the scene and provide more accurate predictions. Additionally, techniques such as optical flow estimation can be used to track feature movements between frames, enabling the module to account for dynamic elements in the environment.

What are the potential limitations of the cosine similarity-based approach for uncertainty estimation, and how could alternative techniques be explored

One potential limitation of the cosine similarity-based approach for uncertainty estimation is its sensitivity to variations in lighting conditions, viewpoints, and image quality. In scenes with complex lighting or occlusions, the cosine similarity may not accurately capture the similarity between feature embeddings. To address this limitation, alternative techniques such as using learned distance metrics or incorporating attention mechanisms to focus on relevant features could be explored. Additionally, ensemble methods that combine multiple similarity metrics or uncertainty estimation models could enhance the robustness of the approach.

Given the flexibility of the HR-APR framework, how could it be adapted to incorporate other types of sensor data (e.g., depth, inertial) to further improve camera relocalisation accuracy and robustness

The flexibility of the HR-APR framework allows for the integration of other sensor data to improve camera relocalization accuracy and robustness. To incorporate depth information, depth estimation networks such as monocular depth estimation models could be integrated into the feature extraction process to provide depth-aware feature embeddings. Inertial sensor data, such as gyroscope and accelerometer readings, could be fused with visual data using sensor fusion techniques like Kalman filters or particle filters to enhance pose estimation accuracy, especially in dynamic environments. By combining multiple modalities of sensor data, the HR-APR framework can offer a more comprehensive and reliable camera relocalization solution.