Tracing commodities in indoor environments for service robotics

Daily life assistance for elderly people is one of the most promising scenarios for service robots in the the near future. In particular, the go-and-fetch task will be one of the most demanding tasks in these cases. In this paper, we present an informationally structured room that supports a service robot in the task of daily object fetching. Our environment contains different distributed sensors including a floor sensing system and several intelligent cabinets. Sensor information is send to a centralized management system which process the data and make it available to a service robot which is assisting people in the room. We additionally present the first steps of an intelligent framework used to maintain information about locations of commodities in our informationally structured room. This information will be used by the service robot to find objects under people requests. One of the main goal of our intelligent environment is to maintain a small number of sensors to avoid interfering with the daily activity of people, and to reduce as much as possible invasion of their privacy. In order to compensate this limited available sensor information, our framework aims to exploit knowledge about people's activity and interaction with objects, to infer reliable information about the location of commodities. This paper presents simulated results that demonstrate the suitability of this framework to be applied to a service robotic environment equipped with limited sensors. In addition we discuss some preliminary experiments using our real environment and robot

The intelligent room for elderly care

Daily life assistance for elderly is one of the most promising and interesting scenarios for advanced technologies in the present and near future. Improving the quality of life of elderly is also some of the first priorities in modern countries and societies where the percentage of elder people is rapidly increasing due mainly to great improvements in medicine during the last decades. In this paper, we present an overview of our informationally structured room that supports daily life activities of elderly. Our environment contains different distributed sensors including a floor sensing system and several intelligent cabinets. Sensor information is sent to a centralized management system which processes the data and makes it available to a service robot which assists the people in the room. One important restriction in our intelligent environment is to maintain a small number of sensors to avoid interfering with the daily activities of people and to reduce as much as possible the invasion of their privacy. In addition we discuss some experiments using our real environment and robot

Laser-based geometric modeling using cooperative multiple mobile robots

Abstract—In order to construct three-dimensional shape models of large-scale architectural structures using a laser range finder, a number of range images are taken from various viewpoints. These images are aligned using post-processing procedures such as the ICP algorithm. However, in general, before applying the ICP algorithm, these range images must be aligned roughly by a human operator in order to converge to precise positions. The present paper proposes a new modeling system using a group of multiple robots and an on-board laser range finder. Each measurement position is identified by a highly precise positioning technique called Cooperative Positioning System (CPS), which utilizes the characteristics of the multiple-robot system. Thus, the proposed system can construct 3D shapes of large-scale architectural structures without any post-processing procedure or manual registration. ICP is applied optionally for a subsequent refinement of the model. Measurement experiments in unknown and large indoor/outdoor environments are carried out successfully using the newly developed measurement system consisting of three mobile robots named CPS-V. Generating a model of Dazaifu Tenmangu, a famous cultural heritage, for its digital archive completes the paper. I

Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

This paper presents an approach to categorize typical places in indoor environments using 3D scans provided by a laser range finder. Examples of such places are offices, laboratories, or kitchens. In our method, we combine the range and reflectance data from the laser scan for the final categorization of places. Range and reflectance images are transformed into histograms of local binary patterns and combined into a single feature vector. This vector is later classified using support vector machines. The results of the presented experiments demonstrate the capability of our technique to categorize indoor places with high accuracy. We also show that the combination of range and reflectance information improves the final categorization results in comparison with a single modality

SARS Risk Perceptions in Healthcare Workers, Japan

In coping with severe acute respiratory syndrome (SARS), infection control measures are a key aspect of protecting healthcare workers. We conducted a survey concerning perception of risk and countermeasures for SARS in 7 tertiary hospitals in Japan from July through September 2003, immediately after the SARS epidemic in neighboring countries. Based on 7,282 respondents out of 9,978 questionnaires administered, we found the perception of risk to be relatively high and the perception of countermeasures at the institutional level to be relatively low. Knowledge of preventive measures, concept of (opinions regarding) institutional measures, and perception of risk differed substantially among the 3 job categories, notably between physicians and nurses. The concept of institutional measures was the most important predictor of individual perception of risk. In view of the potential for future epidemics, planning and implementing institutional measures should be given a high priority

Categorization of indoor places using the Kinect sensor

The categorization of places in indoor environments is an important capability for service robots working and interacting with humans. In this paper we present a method to categorize different areas in indoor environments using a mobile robot equipped with a Kinect camera. Our approach transforms depth and grey scale images taken at each place into histograms of local binary patterns (LBPs) whose dimensionality is further reduced following a uniform criterion. The histograms are then combined into a single feature vector which is categorized using a supervised method. In this work we compare the performance of support vector machines and random forests as supervised classifiers. Finally, we apply our technique to distinguish five different place categories: corridors, laboratories, offices, kitchens, and study rooms. Experimental results show that we can categorize these places with high accuracy using our approach

Development of an intravital imaging system for the synovial tissue reveals the dynamics of CTLA-4 Ig in vivo

There have been many attempts to visualize the inflamed joints using multiphoton microscopy. However, due to the hypervascular and multilayered structure of the inflamed synovium, intravital imaging of the deep synovial tissue has been difficult. Here, we established original intravital imaging systems to visualize synovial tissue and pathological osteoclasts at the pannus–bone interface using multiphoton microscopy. Combined with fluorescence-labeling of CTLA-4 Ig, a biological agent used for the treatment of rheumatoid arthritis, we identified that CTLA-4 Ig was distributed predominantly within the inflamed synovium and bound to CX3CR1+ macrophages and CD140a+ fibroblasts 6 h after injection, but not to mature osteoclasts. Intravital imaging of blood and lymphatic vessels in the inflamed synovium further showed that extravasated CTLA-4 Ig was immediately drained through lymphatic vessels under acute arthritic conditions, but the drainage activity was retarded under chronic conditions. These results indicate that this intravital synovial imaging system can serve as a platform for exploring the dynamics of immune cells, osteoclasts, and biological agents within the synovial microenvironment in vivo.Hasegawa T., Kikuta J., Sudo T., et al. Development of an intravital imaging system for the synovial tissue reveals the dynamics of CTLA-4 Ig in vivo. Scientific Reports 10, 13480 (2020); https://doi.org/10.1038/s41598-020-70488-y

Robust 2D-3D alignment based on geometrical consistency

This paper presents a new registration algorithm of a 2D image and a 3D geometrical model, which is robust for initial registration errors, for reconstructing a realistic 3D model of indoor scene settings. One of the typical tech-niques of pose estimation of a 3D model in a 2D image is the method based on the correspondences between 2D pho-tometrical edges and 3D geometrical edges projected on the 2D image. However, for indoor settings, features extracted on the 2D image and jump edges of the geometrical model, which can be extracted robustly, are limited. Therefore, it is difficult to find corresponding edges between the 2D image and the 3D model correctly. For this reason, in most cases, the relative position has to be manually set close to correct position beforehand. To overcome this problem, in the pro-posed method, firstly the relative pose is roughly estimated by utilizing geometrical consistencies of back-projected 2D photometrical edges on a 3D model. Next, the edge-based method is applied for the precise pose estimation after the above estimation procedure is converged. The performance of the proposed method is successfully demonstrated with some experiments using simulated models of indoor scene settings and actual environments measured by range and image sensors. 1