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For the unmanned vehicle, multi-line LiDAR (Light Detection and Ranging) and GPS/IMU are often used in conjunction for SLAM or the production of high-precision maps. To improve the accuracy of navigation as well as map building, the extrinsic parameters calibration of LiDAR and GPS/IMU is often required. In response to the problem of insufficient conditions in the calibration of LiDAR and GPS/IMU for unmanned vehicle, this paper proposes a decoupling method, grouping the extrinsic parameters for calibration, and finally merges the point clouds with the pose obtained by the GPS/IMU before and after calibration to get environment reconstruction results. With the reconstruction results before and after calibration, it is verified that the method proposed is effective.

A tightly coupled LIDAR-IMU SLAM is proposed in this paper to make precise and robust estimation on position, posture, velocity as well as bias of accelerometers and gyros in off-road environment where features are not sufficient. This method is based on optimization of residuals both produced by LIDAR point clouds and IMU integration. The first part of residuals comes from the sum of distance between current sweep point clouds and voxel centroid of relative maps which are built simultaneously. The second part of residuals comes from a pre-integration procedure which takes LIDAR and IMU calibration error into account. A series of experiments based on data collected from an intelligent vehicle platform is carried out to evaluate the SLAM system. The experimental results have proven the ability of the system for precise pose estimation. Compared with the LIDAR-only method, LIDAR-IMU SLAM shows better performance on the estimation accuracy and robustness on position and posture as well as obtaining convergent result of pitch and roll angle.

This study is concerned with an efficiency analysis through data produced from a business simulation game at Osaka International University(OIU). We concentrate on two factors, namely selecting evaluating items and improving the range of the growth potential efficiency. The financial ratio analysis and the sensitivity analysis are used to propose improvement. The objective of this study is to propose a method that can make up for shortfalls of conventional financial ratio analysis methods and find specific items to improve through multi-steps using the CCR(Charnes, Cooper and Rhodes) model of DEA.

We propose the problems of multiple Decision Maker(DM)s' consensus building in this paper. The problems are classified two models from viewpoints of decision makers' pattern. First model has Super Decision Maker(SDM) and DMs. Second Model has only DMs. The former model with SDM and DMs is corresponded to the traditional Analytic Hierarchy Process(AHP) model which can be solved the AHP method. The former model with SDM and DMs is also called the Group-AHP model. The latter model called group-DEAHP with only DMs is solved by the Data Envelopment Analysis(DEA). This paper focus on latter model, the efficient solution procedure is proposed based on the grey system theory.