Layered Cost-Map-Based Traffic Management for Multiple Automated Mobile Robots via a Data Distribution Service

This letter proposes traffic management for multiple automated mobile robots (AMRs) based on a layered cost map. Multiple AMRs communicate via a data distribution service (DDS), which is shared by topics in the same DDS domain. The cost of each layer is manipulated by topics. The traffic management server in the domain sends or receives topics to each of AMRs. Using the layered cost map, the new concept of prohibition filter, lane filter, fleet layer, and region filter are proposed and implemented. The prohibition filter can help a user set an area that would prohibit an AMR from trespassing. The lane filter can help set one-way directions based on an angle image. The fleet layer can help AMRs share their locations via the traffic management server. The region filter requests for or receives an exclusive area, which can be occupied by only one AMR, from the traffic management server. All the layers are experimentally validated with real-world AMRs. Each area can be configured with user-defined images or text-based parameter files.Comment: 8 pages, 13 figure

Differentiable Display Photometric Stereo

Photometric stereo leverages variations in illumination conditions to reconstruct per-pixel surface normals. The concept of display photometric stereo, which employs a conventional monitor as an illumination source, has the potential to overcome limitations often encountered in bulky and difficult-to-use conventional setups. In this paper, we introduce Differentiable Display Photometric Stereo (DDPS), a method designed to achieve high-fidelity normal reconstruction using an off-the-shelf monitor and camera. DDPS addresses a critical yet often neglected challenge in photometric stereo: the optimization of display patterns for enhanced normal reconstruction. We present a differentiable framework that couples basis-illumination image formation with a photometric-stereo reconstruction method. This facilitates the learning of display patterns that leads to high-quality normal reconstruction through automatic differentiation. Addressing the synthetic-real domain gap inherent in end-to-end optimization, we propose the use of a real-world photometric-stereo training dataset composed of 3D-printed objects. Moreover, to reduce the ill-posed nature of photometric stereo, we exploit the linearly polarized light emitted from the monitor to optically separate diffuse and specular reflections in the captured images. We demonstrate that DDPS allows for learning display patterns optimized for a target configuration and is robust to initialization. We assess DDPS on 3D-printed objects with ground-truth normals and diverse real-world objects, validating that DDPS enables effective photometric-stereo reconstruction

Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild

Social media platforms struggle to protect users from harmful content through content moderation. These platforms have recently leveraged machine learning models to cope with the vast amount of user-generated content daily. Since moderation policies vary depending on countries and types of products, it is common to train and deploy the models per policy. However, this approach is highly inefficient, especially when the policies change, requiring dataset re-labeling and model re-training on the shifted data distribution. To alleviate this cost inefficiency, social media platforms often employ third-party content moderation services that provide prediction scores of multiple subtasks, such as predicting the existence of underage personnel, rude gestures, or weapons, instead of directly providing final moderation decisions. However, making a reliable automated moderation decision from the prediction scores of the multiple subtasks for a specific target policy has not been widely explored yet. In this study, we formulate real-world scenarios of content moderation and introduce a simple yet effective threshold optimization method that searches the optimal thresholds of the multiple subtasks to make a reliable moderation decision in a cost-effective way. Extensive experiments demonstrate that our approach shows better performance in content moderation compared to existing threshold optimization methods and heuristics.Comment: WSDM2023 (Oral Presentation

Acousto-optic volumetric gating for reflection-mode deep optical imaging within a scattering medium

The imaging depth of deep-tissue optical microscopy is governed by the performance of the gating operation that suppresses the multiply scattered waves obscuring the ballistic waves. Although various gating operations based on confocal, time-resolved/coherence-gated, and polarization-selective detections have proven to be effective, each has its own limitation; certain types of multiply scattered waves can bypass the gating. Here, we propose a method, volumetric gating, that introduces ultrasound focus to confocal reflectance imaging to suppress the multiply scattered waves traveling outside the ultrasonic focal volume. The volumetric gating axially rejects the multiply scattered wave traveling to a depth shallower than the object plane while suppressing the deeper penetrating portion that travels across the object plane outside the transversal extent of the ultrasonic focus of 30${\times}$90${\mu}m^2$. These joint gating actions along the axial and lateral directions attenuate the multiply scattered waves by a factor of 1/1000 or smaller, thereby extending the imaging depth to 12.1 times the scattering mean free path while maintaining the diffraction-limited resolution of 1.5 ${\mu}$m. We demonstrated an increase in the imaging depth and contrast for internal tissue imaging of mouse colon and small intestine through their outer walls. We further developed theoretical and experimental frameworks to characterize the axial distribution of light trajectories inside scattering media. The volumetric gating will serve as an important addition to deep-tissue imaging modalities and a useful tool for studying wave propagation in scattering media.Comment: 18 pages, 5 figure