Geometry-Aware Learning of Maps for Camera Localization (1712.03342v3)

Abstract: Maps are a key component in image-based camera localization and visual SLAM systems: they are used to establish geometric constraints between images, correct drift in relative pose estimation, and relocalize cameras after lost tracking. The exact definitions of maps, however, are often application-specific and hand-crafted for different scenarios (e.g. 3D landmarks, lines, planes, bags of visual words). We propose to represent maps as a deep neural net called MapNet, which enables learning a data-driven map representation. Unlike prior work on learning maps, MapNet exploits cheap and ubiquitous sensory inputs like visual odometry and GPS in addition to images and fuses them together for camera localization. Geometric constraints expressed by these inputs, which have traditionally been used in bundle adjustment or pose-graph optimization, are formulated as loss terms in MapNet training and also used during inference. In addition to directly improving localization accuracy, this allows us to update the MapNet (i.e., maps) in a self-supervised manner using additional unlabeled video sequences from the scene. We also propose a novel parameterization for camera rotation which is better suited for deep-learning based camera pose regression. Experimental results on both the indoor 7-Scenes dataset and the outdoor Oxford RobotCar dataset show significant performance improvement over prior work. The MapNet project webpage is https://goo.gl/mRB3Au.

• The paper introduces a novel geometry-aware framework, MapNet, that integrates geometric constraints from VO, GPS, and IMU data to enhance camera localization accuracy.
• It leverages self-supervised MapNet+ techniques to continuously refine map representations using unlabeled videos in dynamic environments.
• Pose Graph Optimization and improved rotation parameterization with unit quaternions further enhance precise, drift-free localization in diverse indoor and outdoor settings.

An Essay on "Geometry-Aware Learning of Maps for Camera Localization"

The paper "Geometry-Aware Learning of Maps for Camera Localization" introduces an innovative approach to map representation in the context of camera localization by leveraging deep neural networks (DNNs). The work proposes a novel method, termed MapNet, which reconstructs map representations directly from input data. This methodology shifts traditional mapping paradigms toward a more adaptable, data-driven strategy, particularly beneficial when addressing visual SLAM (Simultaneous Localization and Mapping) and image-based localization challenges.

Summary of Contributions

The primary contribution of MapNet is the introduction of a geometry-aware learning mechanism, where geometric constraints traditionally applied during bundle adjustment or pose-graph optimization are integrated into the DNN learning process. Specifically, the strengths of MapNet are encapsulated as follows:

• Geometric Constraint Integration: Rather than purely relying on absolute pose data for training, MapNet incorporates relative geometric constraints from visual odometry (VO) readings, GPS data, and potentially IMU data into its loss function. This integration allows the network to leverage the spatial relationships between image pairs during training, which empirical results show leads to improved localization accuracy.
• Self-supervised Learning through MapNet+: By extending MapNet into MapNet+, the authors introduce a continuous learning paradigm where distorted weights can be updated using additional unlabeled videos. Such self-supervised learning is facilitated by the inclusion of geometric constraints between observation pairs. This adaptability is a significant advancement, allowing MapNet+ to refine new information as environments evolve over time.
• Pose Graph Optimization (PGO) during Inference: The refinement of pose predictions in a moving-window approach through MapNet+PGO combines the drift-free nature of MapNet predictions with the local accuracy of VO. This PGO facilitates improvements in the accuracy of pose estimation.
• Enhanced Parameterization for Rotation in Pose Regression: The research proposes using the logarithm of unit quaternions, which offers a more direct and efficient technique for representing rotations in DNN-based camera pose regression. This parameterization avoids the normalization dilemma present with standard quaternion outputs.

Empirical Evaluation

The empirical evaluation carried out on both indoor and outdoor datasets, such as the 7-Scenes and Oxford RobotCar datasets, underscores MapNet’s significant improvements over traditional methods. In terms of results:

• The introduction of geometry-aware learning resulted in a dramatic decrease in localization error. When compared to baseline models like PoseNet and traditional VO methods, MapNet achieves superior precision.
• MapNet+, through the assimilation of unlabeled data, prepares the model for practical scenarios where labeled data is sparse, showcasing its utility by fine-tuning and improving accuracy in real-world applications.
• The benefits of Pose Graph Optimization are demonstrated in enhanced temporal smoothness and reduced drift in predictions, validating the effectiveness of combining the absolute positioning capability of MapNet with the relative precision of visual and sensory inputs.

Future Directions and Implications

The implications of this research are extensive. The ability to continuously optimize map representations as more data becomes available positions MapNet as a potentially valuable component in SLAM systems which operate in dynamic, evolving environments. However, one limitation noted is MapNet's current inability to extend maps into previously unobserved areas, a potential avenue for future research. An integration of high-level semantic data such as object detection into this framework may result in improved localization.

In conclusion, this paper presents a significant step forward in camera localization by marrying the fields of DNN-based learning and geometric mapping. MapNet stands as a promising avenue for improving localization systems in autonomous vehicles and augmented reality, pointing towards a future where data-driven methods can outperform traditional handcrafted techniques. Future developments may explore the extension of such models within broader areas of uncharted territories, particularly through a symbiosis with rich semantic understanding.