Autonomous mapping systems execute multiple tasks that include navigation, location, and map generation via the collaborative work of multiple sensors. They are the object of a substantial research focus in the fields of robotics and remote sensing. Although the state-of-the-art mobile mapping systems typically found in readymade vehicles or robots are reliable, they are rather large and heavy, their cost is high, and they generally use GPS and an inertial measurement unit to position, so their working environments are limited. After reviewing the current state of autonomous mapping systems, we describe the design and development of a small and lightweight autonomous mapping system (ASQ-6DMapSys) without GPS, which incorporates low-cost sensors and components. We describe the layout and selection strategy for sensors and other components in detail, and we present the design methodology for each subsystem. The ASQ-6DMapSys employs a two-dimensional (2D) lidar, an inclinometer, and two wheel encoders, which constitute a pose subsystem that uses extended Kalman filtering and simultaneous localization and mapping techniques to compute the pose of the vehicle body. A lowcost 3D lidar that we developed is also installed on the vehicle body, and the resultant data are aligned with the corresponding pose data of the vehicle body to build a 3D point cloud that describes the global geometry of the environment. We designed and developed every subsystem of the ASQ-6DMapSys, including the robot vehicle, so it will be easy to expand its functions in the future. The ASQ-6DMapSys performs well in indoor, outdoor, and tunnel environments, and the experiments in different environments show that the ASQ-6DMapSys is an effective, small, and lightweight autonomous mapping system with a high performance/price ratio. C 2013 Wiley Periodicals, Inc.
Spatial transcriptomics (ST) technologies detect mRNA expression in single cells/spots while preserving their two-dimensional (2D) spatial coordinates, allowing researchers to study the spatial distribution of the transcriptome in tissues; however, joint analysis of multiple ST slices and aligning them to construct a three-dimensional (3D) stack of the tissue still remain a challenge. Here, we introduce spatial architecture characterization by deep learning (SPACEL) for ST data analysis. SPACEL comprises three modules: “Spoint” embeds a multiple-layer perceptron with a probabilistic model to deconvolute cell type composition for each spot in a single ST slice; “Splane” employs a graph convolutional network approach and an adversarial learning algorithm to identify spatial domains that are transcriptomically and spatially coherent across multiple ST slices; and “Scube” automatically transforms the spatial coordinate systems of consecutive slices and stacks them together to construct a 3D architecture of the tissue. Comparisons against 16 state-of-the-art methods using both simulated and real ST datasets demonstrate that SPACEL outperforms the others for cell type deconvolution, for spatial domain identification, and for 3D alignment, thus showcasing SPACEL as a valuable integrated toolkit for ST data processing and analysis.
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