Inspecting large-scale industrial surfaces like aircraft fuselages for quality control requires precise, high-resolution 3D geometry. Vision-based tactile sensors (VBTSs) offer high local resolution but require slow 'press-and-lift' measurements for large areas. Sliding or roller/belt VBTS designs provide continuous measurement but face significant challenges: sliding suffers from friction/wear, while both are speed-limited by camera frame rates and motion blur. Thus, a rapid, continuous, high-resolution method is needed. We introduce a novel neuromorphic tactile roller sensor. It uses a modified event-based multi-view stereo algorithm for 3D reconstruction, leveraging high temporal resolution and motion blur robustness. This reconstruction is most effective for surfaces with distinct edges or sharp features, which are often the most critical for defect detection in industrial inspection tasks. We demonstrate 0.5 m/s scanning speeds with MAE below 100$\mu$m (11x faster than prior methods). A multi-reference Bayesian fusion strategy reduces MAE by 25.2% (vs. EMVS) and mitigates curvature errors. We also validate high-speed Braille reading 2.6x faster than previous approaches.

Event cameras triggered a paradigm shift in the computer vision community delineated by their asynchronous nature, low latency, and high dynamic range. Calibration of event cameras is always essential to account for the sensor intrinsic parameters and for 3D perception. However, conventional image-based calibration techniques are not applicable due to the asynchronous, binary output of the sensor. The current standard for calibrating event cameras relies on either blinking patterns or event-based image reconstruction algorithms. These approaches are difficult to deploy in factory settings and are affected by noise and artifacts degrading the calibration performance. To bridge these limitations, we present E-Calib, a novel, fast, robust, and accurate calibration toolbox for event cameras utilizing the asymmetric circle grid, for its robustness to out-of-focus scenes. E-Calib introduces an efficient reweighted least squares (eRWLS) method for feature extraction of the calibration pattern circles with sub-pixel accuracy and robustness to noise. In addition, a modified hierarchical clustering algorithm is devised to detect the calibration grid apart from the background clutter. The proposed method is tested in a variety of rigorous experiments for different event camera models, on circle grids with different geometric properties, on varying calibration trajectories and speeds, and under challenging illumination conditions. The results show that our approach outperforms the state-of-the-art in detection success rate, reprojection error, and pose estimation accuracy.

Neuromorphic vision is a bio-inspired technology that has triggered a paradigm shift in the computer vision community and is serving as a key enabler for a wide range of applications. This technology has offered significant advantages, including reduced power consumption, reduced processing needs, and communication speedups. However, neuromorphic cameras suffer from significant amounts of measurement noise. This noise deteriorates the performance of neuromorphic event-based perception and navigation algorithms. In this article, we propose a novel noise filtration algorithm to eliminate events that do not represent real log-intensity variations in the observed scene. We employ a graph neural network (GNN)-driven transformer algorithm, called GNN-Transformer, to classify every active event pixel in the raw stream into real log-intensity variation or noise. Within the GNN, a message-passing framework, referred to as EventConv, is carried out to reflect the spatiotemporal correlation among the events while preserving their asynchronous nature. We also introduce the known-object ground-truth labeling (KoGTL) approach for generating approximate ground-truth labels of event streams under various illumination conditions. KoGTL is used to generate labeled datasets, from experiments recorded in challenging lighting conditions, including moon light. These datasets are used to train and extensively test our proposed algorithm. When tested on unseen datasets, the proposed algorithm outperforms state-of-the-art methods by at least 8.8% in terms of filtration accuracy. Additional tests are also conducted on publicly available datasets (ETH Zürich Color-DAVIS346 datasets) to demonstrate the generalization capabilities of the proposed algorithm in the presence of illumination variations and different motion dynamics. Compared to state-of-the-art solutions, qualitative results verified the superior capability of the proposed algorithm to eliminate noise while preserving meaningful events in the scene.

Vision-based control of unmanned aerial vehicles (UAVs) has been adopted in a wide range of applications due to the availability of low-cost onboard sensors and computers. Tuning such systems to work properly requires extensive domain-specific experience, which limits the growth of emerging applications. Moreover, obtaining performance limits for UAVs performing visual servoing is difficult due to the complexity of the models used. In this article, we propose a novel noise-tolerant approach for real-time identification and tuning of visual servoing systems. This is based on the deep neural networks (DNNs) classification of the system response generated by the modified relay feedback test (MRFT). The proposed method, called DNN with noise-protected MRFT (DNN-NP-MRFT), can be used with a multitude of vision sensors and estimation algorithms despite high levels of sensor noise. The response of DNN-NP-MRFT to noise perturbations is investigated and its effect on identification and tuning performance is analyzed. The proposed DNN-NP-MRFT is able to detect performance changes induced by the use of high latency vision sensors or by integrating an inertial measurement unit sensor into the UAV states estimation pipeline. Experimental identification closely matches simulation results, which can be used to explain the system behavior and predict the closed-loop performance limits for a given hardware and software setup. We also demonstrate the ability of DNN-NP-MRFT-tuned UAVs to reject external disturbances like wind or human push and pull. Finally, we discuss the advantages of the proposed DNN-NP-MRFT visual servoing design approach compared with other approaches in the literature.