Valentina Cavinato

Suman Ghosh, Valentina Cavinato, Guillermo Gallego

Visual Odometry (VO) and SLAM are fundamental components for spatial perception in mobile robots. Despite enormous progress in the field, current VO/SLAM systems are limited by their sensors' capability. Event cameras are novel visual sensors that offer advantages to overcome the limitations of standard cameras, enabling robots to expand their operating range to challenging scenarios, such as high-speed motion and high dynamic range illumination. We propose a novel event-based stereo VO system by combining two ideas: a correspondence-free mapping module that estimates depth by maximizing ray density fusion and a tracking module that estimates camera poses by maximizing edge-map alignment. We evaluate the system comprehensively on five real-world datasets, spanning a variety of camera types (manufacturers and spatial resolutions) and scenarios (driving, flying drone, hand-held, egocentric, etc). The quantitative and qualitative results demonstrate that our method outperforms the state of the art in majority of the test sequences by a margin, e.g., trajectory error reduction of 45% on RPG dataset, 61% on DSEC dataset, and 21% on TUM-VIE dataset. To benefit the community and foster research on event-based perception systems, we release the source code and results: https://github.com/tub-rip/ES-PTAM

Yunpu Hu, Fabian Schilling, Valentina Cavinato et al.

Spin plays a crucial role in many ball sports due to its effect on the trajectory of the ball. Vision-based estimation of the ball's spin during a game with conventional cameras is challenging due to the ball's small size, high speed, and fast rotation. To address these challenges, we propose an event-based active vision system that can track unmodified balls and measure their spin in real-time. The system consists of an event camera for its high temporal resolution and minimal motion blur, high-speed pan/tilt galvanometer mirrors to keep the ball in the field of view, and a low-latency focus-tunable telephoto lens to increase the spatial resolution on the ball and keep it in focus. To track the ball, we use a hybrid approach that combines 2D event-based detection for centering and 3D positions from a ball localization system for re-initialization. For high-accuracy spin estimation, we propose an offline method that performs contrast maximization on the sphere (s-CMax). This method achieves state-of-the-art accuracy on static balls across multiple sports (table tennis, baseball, tennis, and golf), with mean magnitude and axis errors of 2.1% and 4.0 degrees, respectively. We then develop a low-latency online method for table tennis as a case study in real-time applications. This method uses an uncertainty-aware convolutional neural network trained on pseudo-ground-truth spin labels from the offline approach, combined with a GPU-accelerated batch implementation of contrast maximization for refinement. We demonstrate reliable tracking and spin estimation with a three-view setup during professional table tennis matches, with high accuracy (8.8% magnitude and 6.4 degrees axis mismatch), 3 ms latency, and 750 Hz throughput.

Valentina Cavinato, Thomas Eppenberger, Dina Youakim et al.

Autonomous exploration allows mobile robots to navigate in initially unknown territories in order to build complete representations of the environments. In many real-life applications, environments often contain dynamic obstacles which can compromise the exploration process by temporarily blocking passages, narrow paths, exits or entrances to other areas yet to be explored. In this work, we formulate a novel exploration strategy capable of explicitly handling dynamic obstacles, thus leading to complete and reliable exploration outcomes in populated environments. We introduce the concept of dynamic frontiers to represent unknown regions at the boundaries with dynamic obstacles together with a cost function which allows the robot to make informed decisions about when to revisit such frontiers. We evaluate the proposed strategy in challenging simulated environments and show that it outperforms a state-of-the-art baseline in these populated scenarios.