Microscopic profilometry based on fringe projection is notable for its noncontact and robust nature and is widely used in industrial electronics. However, its ability to observe electronic packages is limited by the depth of field (DOF) of the lens. This article proposes a novel reconstruction scheme based on telecentric 3-D profilometry to overcome this limitation. The advantage of profilometry based on phase-shifting fringe projection is utilized through defocus detection to achieve high-precision contour measurement with a single DOF. After completing such local reconstruction, a complete 3-D model is registered from partial fragments in different DOFs, making it simple for the microscope system to measure the geometric structure of objects whose thickness exceeds three times the DOF. To accommodate occlusion in local reconstruction, a specific detection algorithm is designed based on intensity variance. Beyond that, we present a novel projection system calibration scheme to improve the calibration robustness and accuracy. Instead of directly using the detected phase to calculate the coordinates of the corners, we first model the relationship between the phase and pixel coordinates and then leverage the estimated parameters to recover the phase. Our proposed method reduces the influence of calibration board spraying errors on the calibration results. Experiments show the effectiveness of this new approach.

Panoptic segmentation plays a crucial role in enabling robots to comprehend their surroundings, providing fine-grained scene understanding information for robots' intelligent tasks. Although existing methods have made some progress, they are prone to fail in areas with weak textures, small objects, etc. Inspired by biological vision research, we propose a cascaded contour-enhanced panoptic segmentation network called CCPSNet, attempting to enhance the discriminability of instances through structural knowledge. To acquire the scene structure, a cascade contour detection stream is designed, which extracts comprehensive scene contours using channel regulation structural perception module and coarse-to-fine cascade strategy. Furthermore, the contour-guided multi-scale feature enhancement stream is developed to boost the discrimination ability for small objects and weak textures. The stream integrates contour information and multi-scale context features through structural-aware feature modulation module and inverse aggregation technique. Experimental results show that our method improves accuracy on the Cityscapes (61.2 PQ) and COCO (43.5 PQ) datasets while also demonstrating robustness in challenging simulated real-world complex scenarios faced by robots, such as dirty cameras and rainy conditions. The proposed network promises to help the robot perceive the real scene. In future work, an unsupervised training strategy for the network could be explored to reduce the training cost.

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.

Accurate localization for intelligent robots remains a significant challenge, and self-supervised visual-inertial odometry (VIO) has emerged as a promising solution. However, existing self-supervised VIO works consider inertial information as the ordinary data input, losing its ability to recover absolute scales and ignoring the modality difference of acceleration and angular velocity in inertial data. In this letter, we present a novel self-supervised VIO framework that augments the odometry-related information implicit in inertial data. For the specific implementation, we propose a self-attention-based IMU network (IMUSAtt) to denoise the raw IMU data and then obtain the poses based on the denoised IMU data through an integrator. By constructing the pose consistency constraint between it and the visual-inertial fused pose, a Self-attention-based Scale Recovery (SSR) module is proposed to recover the absolute scale. Additionally, to avoid the interference of acceleration on rotation estimation, we design a Decoupled PoseNet (D-PoseNet) that employs different inputs and networks to learn rotation and translation. Odometry, scale, and depth evaluations on the KITTI odometry dataset and the Malaga dataset all reveal that our framework achieves state-of-the-art (SOTA) performance.

Spiking neural network (SNN) is an event-driven neural network that can greatly reduce the power consumption of the conventional artificial neural networks (ANN). Many ANN models can be converted to SNN models when the activation function is ReLU. For ANN models with other activation functions, such as the Leaky ReLU function, the converted SNN models either suffer from serious accuracy degradation or require a long time step. In this paper, we propose a fast and accurate ANN-SNN conversion algorithm for models with the Leaky ReLU function. We design a novel neuron model that supports negative spikes. To address the problem of long tail distribution in the activation values, we propose a threshold optimization algorithm based on the variance of the activation values. To avoid the problem of error accumulation, we jointly calibrate all layers in the SNN model with adaptive weighting. Experiment results verify the effectiveness of the proposed algorithm.