The purpose of this study is to improve an accuracy and efficiency of 6DOF motion estimation in 3D MCL for indoor localization. Firstly, a terrestrial laser scanner is used for creating a precise 3D mesh model as an environment map, and a professional-level depth camera is installed as an outer sensor. GPU scene simulation is also introduced to upgrade the speed of prediction phase in MCL. Moreover, for further improvement, GPGPU programming is implemented to realize further speed up of the likelihood estimation phase, and anisotropic particle propagation is introduced into MCL based on the observations from an inertia sensor. Improvements in the localization accuracy and efficiency are verified by the comparison with a previous MCL method. The results showed that our proposed 3D MCL method outperforms the previous one in accuracy and efficiency.
Getting both “results” such as POS (Point of Sales) data and “processes” including spatio-temporal data on human behavior and environmental stimuli and constraints in an actual service field, it makes the field virtually tangible. Such tangibility must be a key driver for understanding what happened there and why it happened more comprehensively. The virtual tangibility can be realized by technologies and methodologies that support the idea of “Lab-forming Field” and “Field-forming Lab” such as IoT (Internet of Things), WoT (Web of Things), and MR (Mixed Reality) encompassing VR (Virtual Reality), AV (Augmented Virtuality), and AR (Augmented Reality). This paper will present “Geospatial IoT (G-IoT)” related technologies and methodologies we have developed and applied to the actual cases to realize “Lab-forming Field”.
Recently, drone is expected to perform various tasks, such as transferring payloads, inspection of infrastructure, and searching and rescuing in disaster areas. It is also further expected for the drone to work indoors where humans cannot easily access. Indoor space is severe condition for the drone to autonomously fly due to limited space and GPS-denied environment and so on. To achieve required task in such an environment, sophisticated drone technology is necessary. This paper introduces research for indoor drone.
On this study, it was discuss about possibility of extraction of the leaf area density difference between tree species, such as Japanese walnut, White willow and Robinia pseudoacasia, from LiDAR intensity information. It was calculated canopy leaf area ratio in order to grasp the tree species difference. The canopy leaf area ratio was calculated by effective irradiation area versus unit irradiation area using a reflectance intensity of the airborne LiDAR. The canopy leaf area ratio was a difference in leaf area of a single leaf. Possibility of tree species identification was suggested by difference of the canopy leaf area ratio.
Japan Aerospace Exploration Agency (JAXA) is operating Sentinel Asia with Asian Disaster Reduction Center (ADRC) and participating in International Charter to contribute to DRR (Disaster Risk Reduction) activities with disaster observation by satellites. International Charter is a framework of international space organizations, and aiming at providing observation data based on requests from designated organizations. 40－50 disasters were covered every year and the total number of the disaster observation until August 2016 has reacheed 500 since its establishment. Also cooperation between International Charter and Sentinel Asia is introduced.