In the mass production of electronic products, in-circuit-test (ICT) and printed circuit board assembly (PCBA) quality tests are performed. ICT measures resistance values and capacitance, but not only does it require the use of a fixture that is expensive and requires frequent replacement but also the fixture’s needles may cause PCBA defects. To overcome these limitations, various studies tried to replace ICT using visual inspection methods; however, visual inspection methods cannot be applied to chip resistors and chip capacitors that do not have externally visible characteristics. In this article, we propose a contactless inspection method that can detect PCBA defects without the use of the fixture and ICT by using the comparison of thermal images and deep learning (DL) analysis. We review the existing contactless inspection methods and compare them with our proposed thermal image analysis method. We analyzed thermal images by applying a structural similarity index map as a rule-based object detection method, and we used convolutional neural networks (CNNs), regions with CNN features, and an autoencoder as DL analysis methods. As a result, we achieved highly accurate defective component detection and location in real time.

In this paper, we present a driverless taxi system for autonomous campus mobility services. College campuses have unique mobility requirements in terms of layout, population, and demand and patterns. It is typically recommended to minimize the presence of private automobiles on campuses due to teaching and research disturbances, visual degradation from parking provision, environmental pollution, and negative health effects. As an alternative to private automobiles, shared mobility systems have been considered for both campus and urban transportation. Conventional shuttle systems suffer from the first and last mile problem. A bicycle and pedestrian friendly policy is not a generalizable solution for all geographic locations and campus layouts. We suggest a driverless taxi service as an alternative point-to-point shared mobility system for campuses. We have demonstrated the feasibility of this service on a 4.5-km campus road at Seoul National University. The service has covered over 10 000 km autonomously since the first public demonstration was made in November 2015.

In LIght Detection And Ranging (LIDAR)-based object detection, accurate object segmentation is of great importance, since segmentation is an essential preprocessing step for other perception tasks, such as classification and tracking. For segmenting objects, most of the previous methods have tried to eliminate the ground first, which typically incurs considerable overhead in computation and inaccuracy in object detection with point clouds gathered by using 3-D LIDARs. However, in many real-time applications, such as automated driving, segmentation should be performed within a specified time, because even a small delay in computation could result in vehicle collisions. In this paper, we propose a real-time and accurate object segmentation algorithm for 3-D point clouds, which does not carry out ground extraction as a first step. In the proposed algorithm, we generate candidate points of objects and find their borders based on the integrated structure of a 2-D grid and an undirected graph, which enables fast processing and yields an accurate segmentation result independent of ground extraction error. In order to enhance segmentation accuracy, we employ Gaussian process, which reduces over-segmentation that separates an object into multiple portions. We apply two types of Gaussian process models to alternately provide cues for merging adjacent over-segmented objects. Experimental results demonstrate that this paper achieves a real-time processing speed and higher segmentation accuracy than previous works in most evaluation metrics. With the application to tracking, we show that the enhanced segmentation accuracy increases the tracking accuracy by 11.4% even in the worst case.

We present an accurate and robust image-to-point cloud registration method that is viable in urban and off-road environments. Existing image-to-point cloud registration methods have focused on vehicle platforms along paved roads. Therefore, image-to-point cloud registration on UGV platforms for off-road driving remains an open question. Our objective is to find a versatile solution for image-to-point cloud registration. We present a method that stably estimates a precise transformation between an image and a point cloud using a two-phase method that aligns the two input data in the virtual reference coordinate system ( virtual-alignment ) and then compares and matches the data to complete the registration ( compare-and-match ). Our main contribution is the introduction of divide-and-conquer strategies to image-to-point cloud registration. The virtual-alignment phase effectively reduces relative pose differences without cross-modality comparison. The compare-and-match phase divides the process of matching the image and point cloud into the rotation and translation steps. By breaking down the registration problem, it is possible to develop algorithms that can robustly operate in various environments. We performed extensive experiments on four datasets (Rellis-3D, KITTI odometry, nuScenes, and KITTI raw). Experiments cover a variety of situations in which image-to-point cloud registration is applied, from image-based localization in off-road environments to camera-LiDAR extrinsic calibration in urban environments. The experiments demonstrate that the proposed method outperforms the existing methods in accuracy and robustness.

Vacant parking‐slot recognition is one of the core elements for achieving a fully automated parking assistant system. The recognition of a vacant parking‐slot is typically executed in two stages: parking‐slot recognition and slot‐occupancy classification. In most previous works, considering the recognised slot results as a grid cell, the existence of objects is evaluated through the slot examination in slot‐occupancy classification. Despite moderate performance, however, these methods cannot distinguish the parking‐slot availability of moving or small objects and permit marks, since all previous methods only focus on the occupancy of typically parked vehicles. In this study, the authors present a camera‐based available parking‐slot recognition algorithm based on slot‐context analysis. By utilising abundant visual features, they propose a new algorithm handling diverse available parking‐slot conditions. For parking‐slot recognition, they propose a new methodology of extracting and associating line‐markings. The proposed algorithm includes a slot‐validation step that identifies multiple slot contexts in a probabilistic integration and has more flexibility on irregular patterns. In the slot‐occupancy classification stage, reliable parking‐availability detection is achieved through visual‐slot features including the histogram of gradient and frequency–magnitude features, via a support vector machine. Simulations and vehicle‐level experiments demonstrate the robustness of the proposed algorithm in diverse conditions.