Accurate 6D object pose estimation is crucial in industrial automation, particularly in robotic bin picking, where objects are often textureless, reflective, and arranged in cluttered environments. Multi-view pose estimation methods offer significant advantages over single-view methods by providing more comprehensive information, effectively handling occlusions and lack of features, and resolving depth ambiguities. However, current multi-view methods often rely on late-stage information fusion, limiting their ability to fully exploit complementary multi-view data. This paper presents a novel approach to enhance multiview 6D pose estimation by introducing a Feature Exchange Transformer (FET) for early-stage feature fusion. This approach leverages self-attention and epipolar cross-attention mechanisms to enable multi-layer feature aggregation across views. Additionally, we introduce a coarse-to-fine strategy for an efficient feature exchange at multiple network layers. Our method, implemented on top of EpiSurfEmb[1], enhances the utilization of multi-view information, leading to significant improvements in pose estimation accuracy and robustness, especially in challenging bin-picking scenarios. We evaluate our approach on the ROBI dataset, demonstrating that it outperforms both the baseline EpiSurfEmb and other state-of-the-art multi-view pose estimation methods.

MOTIVATION: Oxford Nanopore Technologies recently adopted the POD5 file format for storing raw nanopore sequencing data. The information stored in these files provides detailed insights into the sequencing features and enhances the understanding of raw nanopore data. However, the process of visualizing the data can be cumbersome, especially for users without programming skills. To address this issue, we developed the pod5Viewer, a GUI application for inspecting POD5 files. RESULTS: The pod5Viewer offers straightforward access to raw sequencing data and associated metadata in POD5 files. It includes functionalities for viewing, plotting, and exporting individual reads. Designed with user-friendliness in mind, the pod5Viewer is easy to install and use, making it suitable for users with all technical backgrounds. AVAILABILITY AND IMPLEMENTATION: The pod5Viewer is available as open source from the pod5Viewer Github repository (https://github.com/dietvin/pod5Viewer).

SUMMARY: Oxford Nanopore Technologies' (ONT) sequencing platform offers an excellent opportunity to perform real-time analysis during sequencing. This feature allows for early insights into experimental data and accelerates a potential decision-making process for further analysis, which can be particularly relevant in the clinical context. Although some tools for the real-time analysis of DNA-sequencing data already exist, there is currently no application available for differential transcriptome data analysis designed for scientists or physicians with limited bioinformatics knowledge. Here, we introduce NanopoReaTA, a user-friendly real-time analysis toolbox for RNA-sequencing data from ONT. Sequencing results from a running or finished experiment are processed through an R Shiny-based graphical user interface with an integrated Nextflow pipeline for whole transcriptome or gene-specific analyses. NanopoReaTA provides visual snapshots of a sequencing run in progress, thus enabling interactive sequencing and rapid decision making that could also be applied to clinical cases. AVAILABILITY AND IMPLEMENTATION: Github https://github.com/AnWiercze/NanopoReaTA; Zenodo https://doi.org/10.5281/zenodo.8099825.

Sensor guided, automated systems require the composition of various sensors and data processing algorithms to obtain relevant information for performing their task. Many applications have additional requirements such as a certain accuracy, which has to be achieved despite sensor noise and calibration errors. In this paper we model the configuration of perception systems as a planning problem over probabilistic graphical models. We work on a subset of the full configuration space of perceptions systems, specifically the used sensors, data processing algorithms and view poses. Based on a semantic description of the goal, available sensors and data processing algorithms, our system plans perception steps and sensor data fusion autonomously. The planner operates by constructing a factor graph until the accuracy requirements of tasks are fulfilled or unobtainable with the available action set. We validate our approach in an industrial assembly scenario.

Reliable precision grasping for unknown objects is a prerequisite for robots that work in the field of logistics, manufacturing and household tasks. The nature of this task requires a simultaneous solution of a mixture of sub-problems. These include estimating object properties, finding viable grasps and executing grasps without displacement. We propose to explicitly take perceptual uncertainty into account during grasp execution. The underlying object representation is a probabilistic signed distance field, which includes both signed distances to the surface and spatially interpretable variances. Based on this representation, we propose a two-stage grasp generation method, which is specifically designed for generating precision grasps. In order to evaluate the whole approach, we perform extensive real world grasping experiments on a set of hard-to-grasp objects. Our approach achieves 78% success rate and shows robustness to the placement orientation.

In this paper, we present an approach for the probabilistic fusion of 3D sensor measurements. Our fusion algorithm is based on truncated signed distance functions. It explicitly considers the measurement noise by modeling the surface using random variables. Furthermore, our proposed surface model provides an explicit estimation of the spatial uncertainty. The approach can be implemented on a GPU to achieve a high update performance and enable online updates of the model. The approach was evaluated in simulation and using real sensor data. In our experiments, we confirmed that it accurately estimates surfaces from noisy sensor data and that it provides a corresponding estimate of the uncertainty. We could also show that the approach is able to fuse measurements from sensors with different noise characteristics.