We compare event-cameras with fast (global shutter) frame-cameras experimentally, asking: “What is the application domain, in which an event-camera surpasses a fast frame-camera?” Surprisingly, finding the answer has been difficult. Our methodology was to test event- and frame-cameras on generic computer vision tasks where event-camera advantages should manifest. We used two methods: (1) a controlled, cheap, and easily reproducible experiment (observing a marker on a rotating disk at varying speeds); (2) selecting one challenging practical ballistic experiment (observing a flying bullet having a ground truth provided by an ultra-high-speed expensive frame-camera). The experimental results include sampling/detection rates and position estimation errors as functions of illuminance and motion speed; and the minimum pixel latency of two commercial state-of-the-art event-cameras (ATIS, DVS240). Event-cameras respond more slowly to positive than to negative large and sudden contrast changes. They outperformed a frame-camera in bandwidth efficiency in all our experiments. Both camera types provide comparable position estimation accuracy. The better event-camera was limited by pixel latency when tracking small objects, resulting in motion blur effects. Sensor bandwidth limited the event-camera in object recognition. However, future generations of event-cameras might alleviate bandwidth limitations.

Manipulating cluttered cables, hoses or ropes is challenging for both robots and humans. Humans often simplify these perceptually challenging tasks by pulling or pushing tangled cables and observing the resulting motions. We propose to use a similar interactive perception principle to aid robotic cable manipulation. A fundamental building block of such an endeavor is a cable motion segmentation method that densely labels moving cable image pixels. This paper presents MovingCables, a moving cable dataset, which we hope will motivate the development and evaluation of cable motion segmentation algorithms. The dataset consists of real-world image sequences automatically annotated with ground truth segmentation masks and optical flow. In addition, we propose a cable motion segmentation method and evaluate its performance on the new dataset. Code, dataset, and visualizations are available at https://github.com/holesond/movingcables and https://doi.org/10.5281/zenodo.11475246 .

Manipulating tangled hoses, cables, or ropes can be challenging for both robots and humans. Humans often approach these perceptually demanding tasks by pushing or pulling tangled cables and observing the resulting motions. We follow a similar idea to aid robotic cable manipulation. In this letter, we integrate visual and proprioceptive perception to segment a grasped cable by moving it even when the robot or the grasped cable sometimes perturb neighboring cables. We formulate the cable interactive segmentation problem in such a way that our methods do not require robot arm segmentation masks. Furthermore, a novel grasp sampling method can propose new cable grasp points given a partial cable segmentation to improve the segmentation via additional cable-robot interaction. We evaluate the proposed motion correlation (MCor) method on data sequences recorded by our physical robotic setup and show that the method outperforms an earlier motion segmentation (MSeg) baseline.