ProBA: Probabilistic Bundle Adjustment with the Bhattacharyya Coefficient

Classical Bundle Adjustment (BA) methods require accurate initial estimates for convergence and typically assume known camera intrinsics, which limits their applicability when such information is uncertain or unavailable. We propose a novel probabilistic formulation of BA (ProBA) that explicitly models and propagates uncertainty in both the 2D observations and the 3D scene structure, enabling optimization without any prior knowledge of camera poses or focal length. Our method uses 3D Gaussians instead of point-like landmarks and we introduce uncertainty-aware reprojection losses by projecting the 3D Gaussians onto the 2D image space, and enforce geometric consistency across multiple 3D Gaussians using the Bhattacharyya coefficient to encourage overlap between their corresponding Gaussian distributions. This probabilistic framework leads to more robust and reliable optimization, even in the presence of outliers in the correspondence set, reducing the likelihood of converging to poor local minima. Experimental results show that \textit{ProBA} outperforms traditional methods in challenging real-world conditions. By removing the need for strong initialization and known intrinsics, ProBA enhances the practicality of SLAM systems deployed in unstructured environments.

TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset

Event cameras are bio-inspired vision sensors which measure per pixel brightness changes. They offer numerous benefits over traditional, frame-based cameras, including low latency, high dynamic range, high temporal resolution and low power consumption. Thus, these sensors are suited for robotics and virtual reality applications. To foster the development of 3D perception and navigation algorithms with event cameras, we present the TUM-VIE dataset. It consists of a large variety of handheld and head-mounted sequences in indoor and outdoor environments, including rapid motion during sports and high dynamic range scenarios. The dataset contains stereo event data, stereo grayscale frames at 20Hz as well as IMU data at 200Hz. Timestamps between all sensors are synchronized in hardware. The event cameras contain a large sensor of 1280x720 pixels, which is significantly larger than the sensors used in existing stereo event datasets (at least by a factor of ten). We provide ground truth poses from a motion capture system at 120Hz during the beginning and end of each sequence, which can be used for trajectory evaluation. TUM-VIE includes challenging sequences where state-of-the art visual SLAM algorithms either fail or result in large drift. Hence, our dataset can help to push the boundary of future research on event-based visual-inertial perception algorithms.

Event-Based Feature Tracking in Continuous Time with Sliding Window Optimization

We propose a novel method for continuous-time feature tracking in event cameras. To this end, we track features by aligning events along an estimated trajectory in space-time such that the projection on the image plane results in maximally sharp event patch images. The trajectory is parameterized by $n^{th}$ order B-splines, which are continuous up to $(n-2)^{th}$ derivative. In contrast to previous work, we optimize the curve parameters in a sliding window fashion. On a public dataset we experimentally confirm that the proposed sliding-window B-spline optimization leads to longer and more accurate feature tracks than in previous work.