Global Localization: Utilizing Relative Spatio-Temporal Geometric Constraints from Adjacent and Distant Cameras (2312.00500v1)

Abstract: Re-localizing a camera from a single image in a previously mapped area is vital for many computer vision applications in robotics and augmented/virtual reality. In this work, we address the problem of estimating the 6 DoF camera pose relative to a global frame from a single image. We propose to leverage a novel network of relative spatial and temporal geometric constraints to guide the training of a Deep Network for localization. We employ simultaneously spatial and temporal relative pose constraints that are obtained not only from adjacent camera frames but also from camera frames that are distant in the spatio-temporal space of the scene. We show that our method, through these constraints, is capable of learning to localize when little or very sparse ground-truth 3D coordinates are available. In our experiments, this is less than 1% of available ground-truth data. We evaluate our method on 3 common visual localization datasets and show that it outperforms other direct pose estimation methods.

• The paper introduces a novel method that leverages both adjacent and distant frame constraints to enhance global camera localization even with less than 1% of 3D ground-truth data.
• It employs a dual-map strategy by predicting corresponding 3D points and uses the Kabsch algorithm with weighted confidence factors for precise rigid alignment.
• Experiments demonstrate that the method significantly outperforms direct pose regression approaches, offering improved accuracy in challenging real-world scenarios.

Understanding Camera Localization with Networks

Introduction to Camera Re-Localization

Camera re-localization, which involves pinpointing a camera's position and orientation from a single image within a pre-mapped area, is critical for various computer vision tasks like robot navigation and virtual reality applications. With advancements in deep learning, numerous neural network-based approaches have been developed to solve the camera pose estimation problems. Unlike traditional methods that regress directly from image to pose, leveraging geometric information about the scene shows promise for enhancing localization accuracy.

A Novel Approach to Learning Geometry

This research introduces a unique strategy that harnesses relative spatial and temporal geometric constraints to improve training of neural networks for global camera localization. The approach focuses on using not just adjacent temporal information but also data from temporally distant frames, creating a network of geometric constraints over space and time. By integrating constraints from different sequences, the model can be trained even when less than 1% of the 3D ground-truth data is available for an area. This paper's methodology demonstrates superior performance over other direct camera pose estimation methods across various datasets.

Training Deep Networks with Geometric Constraints

The method revolves around predicting two sets of corresponding 3D points: one set relative to the global coordinate frame of the scene and another as observed from the camera viewpoint, using the image depth information. These two sets, or maps, are aligned using a rigid alignment method to estimate the camera's pose. A core innovation of this work is the concurrent application of relative geometric constraints on temporally adjacent and distant frames during training, resulting in a robust training process even with sparse 3D ground-truth data.

Rigid Alignment and Weighted Estimates

Localization is performed by aligning the two learned map representations using the classic Kabsch algorithm in combination with innovatively predicted weighting factors. These weights help to guide the rigid alignment during localization by indicating the confidence of each 3D-3D point correspondence, allowing the model to compensate for imperfections in the learned maps.

Conclusion and Future Directions

The presented method proves suitable for environments where sparse ground-truth data is available and demonstrates significant localization accuracy improvements. A direction for further research could involve integrating this geometric constraint network with a differentiable RANSAC learning scheme to address any remaining limitations associated with the current rigid alignment strategy.