自律移動ロボット用の地図を豊かにする高レベルの知識生成に関する研究

要約のみTohoku University田所諭課

Consistent map building in petrochemical complexes for firefighter robots using SLAM based on GPS and LIDAR

The objective of this study was to achieve simultaneous localization and mapping (SLAM) of firefighter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle filters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The difference between the original Fast-SLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was significant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refinery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability

Triple Graph Grammars or Triple Graph Transformation Systems? A Case Study from Software Configuration Management

Abstract. Triple graph grammars have been used to specify consistency maintenance between inter-dependent and evolving models at a high level of abstraction. On a lower level, consistency maintenance may be specified by a triple graph transformation system, which takes care of all operational details required for executing consistency maintenance operations. This paper presents a case study from software configuration management in which we decided to hand-craft a triple graph transformation system rather than to generate it from a triple graph grammar. The case study demonstrates some limitations concerning the kinds of consistency maintenance problems which can be handled by triple graph grammars

Assessment of treatment change for sexual offenders against children: comparing different methodologies based on psychometric self-report

Communities seek success when it comes to preventing the sexual abuse of children. Thus, how best to measure treatment gains for incarcerated offenders and how those gains are linked to reductions in recidivism are important topics for research. This study examines the relationship between psychometric changes and recidivism in a sample of 495 sex offenders who completed treatment in the prison-based Kia Marama treatment programme in Rolleston prison, New Zealand. The specific goals of this study were threefold. Firstly; to characterise offender progress overall on the administered psychometric battery in terms of five different methods of calculating change. Two methods of calculating clinically significant change were employed. Firstly, change was calculated using the Jacobson and Truax (1991) method of establishing a cut off score based on normative data for each measure. Secondly change was defined as clinically significant when the post treatment score fell 1SD away from the pre-treatment mean in the direction of functionality, a methodology used by Wakeling, Beech, and Freemantle (2013). Two methods of calculating reliable change were then employed. Firstly the Jacobson, Follette, Revenstorf, et al. (1984) calculation adopted by Wakeling et al. (2013) was applied, followed by the more stringent formula proposed by Christensen and Mendoza (1986) which takes account of the standard error of difference. Finally, residual change scores were calculated, replicating the change methodology adopted by Beggs and Grace (2011). The second goal of this study was to compare the five different methodologies above for assessing change based on participants’ scores on the administered psychometric battery. The third and final goal was to determine which of the five identified methods of measuring change demonstrated the strongest correlation with recidivism. Measures of clinically significant change were found to be significantly correlated with recidivism. However, this was not necessarily true when change was defined as both reliable and clinically significant. Results indicated that the Wakeling et al. (2013) method of calculating clinically significant change outperformed all of the others in regards to predicting recidivism. Overall, the present results support the use of self-report psychometrics in measuring treatment change and predicting recidivism

Consistent map building in petrochemical complexes for frefghter robots using SLAM based on GPS and LIDAR

The objective of this study was to achieve simultaneous localization and mapping (SLAM) of frefghter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle flters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The diference between the original FastSLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was signifcant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refnery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability