In recent years, the research of 3D mapping technique in urban environments obtained by mobile robots equipped with multiple sensors for recognizing the robot’s surroundings is being studied actively. However, the map generated by simple integration of multiple sensors data only gives spatial information to robots. To get a semantic knowledge to help an autonomous mobile robot from the map, the robot has to convert low-level map representations to higher-level ones containing semantic knowledge of a scene. Given a 3D point cloud of an urban scene, this research proposes a method to recognize the objects effectively using 3D graph model for autonomous mobile robots. The proposed method is decomposed into three steps: sequential range data acquisition, normal vector estimation and incremental graph-based segmentation. This method guarantees the both real-time performance and accuracy of recognizing the objects in real urban environments. Also, it can provide plentiful data for classifying the objects. To evaluate a performance of proposed method, computation time and recognition rate of objects are analyzed. Experimental results show that the proposed method has efficiently in understanding the semantic knowledge of an urban environment.

A map of complex environment can be generated using a robot carrying sensors. However, representation of environments directly using the integration of sensor data tells only spatial existence. In order to execute high-level applications, robots need semantic knowledge of the environments. This research investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The proposed system is decomposed into five steps: sequential LIDAR scan, point classification, ground detection and elimination, segmentation, and object classification. This method could classify the various objects in urban environment, such as cars, trees, buildings, posts, etc. The simple methods minimizing time-consuming process are developed to guarantee real-time performance and to perform data classification on-the-fly as data is being acquired. To evaluate performance of the proposed methods, computation time and recognition rate are analyzed. Experimental results demonstrate that the proposed algorithm has efficiency in fast understanding the semantic knowledge of a dynamic urban environment.

In this study, Life Cycle Assessment(LCA) has been carried out to evaluate the environmental impacts of a metallic can. A 360 mL volume of an aluminum can bottle was used as the functional unit. The results of Life Cycle Inventory(LCI) showed that iron ore and coal were the major parts of the input materials, whereas aluminum can products, carbon dioxide, wastewater, and hazardous wastes were those of the output ones. According to LCA weighting, it was observed that the most significant impact potential was found to be global warming(49.11%) followed by abiotic resource depletion(47.72%). In the whole system, cold rolled steel coil showed the largest environmental impact potential(86%), followed by electricity(14%). Meanwhile, lubricating oil and industrial water had the minor portion of the total environmental impact potentials. It was suggested that the use of cold rolled steel and electricity should be the main source for CO2, resulting in the big impact on global warming.