Event-based Mosaicing Bundle Adjustment
arXiv:2409.07365 · doi:10.1007/978-3-031-72624-8_27
Abstract
We tackle the problem of mosaicing bundle adjustment (i.e., simultaneous refinement of camera orientations and scene map) for a purely rotating event camera. We formulate the problem as a regularized non-linear least squares optimization. The objective function is defined using the linearized event generation model in the camera orientations and the panoramic gradient map of the scene. We show that this BA optimization has an exploitable block-diagonal sparsity structure, so that the problem can be solved efficiently. To the best of our knowledge, this is the first work to leverage such sparsity to speed up the optimization in the context of event-based cameras, without the need to convert events into image-like representations. We evaluate our method, called EMBA, on both synthetic and real-world datasets to show its effectiveness (50% photometric error decrease), yielding results of unprecedented quality. In addition, we demonstrate EMBA using high spatial resolution event cameras, yielding delicate panoramas in the wild, even without an initial map. Project page: https://github.com/tub-rip/emba
arXiv comment14+11 pages, 11 figures, 10 tables, https://github.com/tub-rip/emba
References in corpus (14)
- Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
- Event-based Vision: A Survey
- The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM
- Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios
- A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation
- Event-aided Direct Sparse Odometry
- CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization
- Globally-Optimal Contrast Maximisation for Event Cameras
- The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data
- Event Collapse in Contrast Maximization Frameworks
- Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow
- Project Aria: A New Tool for Egocentric Multi-Modal AI Research
- A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework
- Event-based Stereo Visual Odometry with Native Temporal Resolution via Continuous-time Gaussian Process Regression