This paper describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping. Accurate mapping of this type of environment is challenging since the ground and the trees are surrounded by leaves, thorns and vines, and the sensor typically experiences extreme motion. We propose a semantic feature based pose optimization that simultaneously refines the tree models while estimating the robot pose. The pipeline utilizes a custom virtual reality tool for labeling 3D scans that is used to train a semantic segmentation network. The masked point cloud is used to compute a trellis graph that identifies individual instances and extracts relevant features that are used by the SLAM module. We show that traditional lidar and image based methods fail in the forest environment on both Unmanned Aerial Vehicle (UAV) and hand-carry systems, while our method is more robust, scalable, and automatically generates tree diameter estimations.

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China possesses an amazing variety of low‐rank coal (LRC) resources. The developed strategies for the large‐scale use of coal resources in China suggest that achieving the grading effective conversion and utilization of LRC by mid–low‐temperature coal‐pyrolysis technologies can maximize LRC resources. On the basis of investigations on and the analysis of coal‐pyrolysis technologies, a comprehensive overview of the LRC pyrolysis technologies in recent years is described in this review. The technologies include square‐retort pyrolysis technology, upgraded square‐retort pyrolysis technology, solid‐heat‐carrier pyrolysis technology, belt‐type‐furnace pyrolysis technology, and rotary‐kiln pyrolysis technology. Focusing on the technical characteristics with analysis and comparison of different LRC pyrolysis technologies, we summarize the main problems in the development of pyrolysis processes, namely, dust separation of coal tar, wastewater treatment, and incomplete pyrolysis reaction mechanisms. Moreover, suitable methods for the rational and efficient utilization of LRC resources are discussed to achieve the sustainable development of pyrolysis technologies based on optimizing pyrolysis reactors and developing deep‐processing multigeneration technologies.

The rapidly increasing demand and complexity of manufacturing process potentiates the usage of manufacturing data with the highest priority to achieve precise analyze and control, rather than using simplified physical models and human expertise. In the era of data-driven manufacturing, the explosion of data amount revolutionized how data is collected and analyzed. This paper overviews the advance of technologies developed for in-process manufacturing data collection and analysis. It can be concluded that groundbreaking sensoring technology to facilitate direct measurement is one important leading trend for advanced data collection, due to the complexity and uncertainty during indirect measurement. On the other hand, physical model-based data analysis contains inevitable simplifications and sometimes ill-posed solutions due to the limited capacity of describing complex manufacturing process. Machine learning, especially deep learning approach has great potential for making better decisions to automate the process when fed with abundant data, while trending data-driven manufacturing approaches succeeded by using limited data to achieve similar or even better decisions. And these trends can demonstrated be by analyzing some typical applications of manufacturing process.