Map estimation using GPS-equipped mobile wireless nodes
Introduction
Situation awareness is the basis of ubiquitous society. We try to sense or capture physical phenomena such as change of temperature and rain, or try to recognize and analyze the forms, locations and behavior of the real world’s objects (such as vehicles and pedestrians) and landscape. We have learned that such situation awareness is also very significant for rescue operations in cases when many people are injured suddenly by a large accident or a disaster in small and condensed regions. For example, in Japan, we experienced a very tragic train accident in 2005 in which over 100 people died and about 460 people were injured. It has been reported that rescue teams need to recognize the positions and condition of patients for efficient rescue operations in such a situation [1]. We have started to design and develop an electronic “triage” system. It continuously senses the vital signs of the patients and estimates their locations by IEEE802.15.4-based wireless sensor networks. We are leading this national project, involving five organizations with several medical doctors and professors in an emergency care department [2].
These doctors say that fast recognition of obstacles such as buildings in the region will be very helpful for rescue operations and treatment actions. It is desirable to generate a local map of the site, which tells us building and street structure information in a city section, the presence of warehouses in a factory, or complicatedly connected small buildings on a university campus. However, such a local and thus detailed map cannot often be obtained from a public map, especially if the region is private property, and even pathways (or streets) may be changed after a disaster. Using digital images of the landscape or range information from radar sensors is a possibility to build a map, but dedicated effort (i.e., taking pictures or measuring ranges at specific points toward specific directions) to obtain such information is required. This encumbers efficient rescue operations since doctors and rescue teams always need manpower for treatment actions. Thus automated acquisition of a local map without dedicated hardware is mandatory in such emergency situation.
In this paper, we propose a local map estimation algorithm for the recognition of an accident site in an emergency situation. We assume that each member in the rescue teams, called a mobile node, is equipped with a GPS receiver and a mid-range communication device such as IEEE802.11 or IEEE802.15.4 that can directly communicate with others several tens of meters away. Since such equipment is very general, the algorithm does not require dedicated devices. The algorithm estimates movable areas and obstacles using position information from GPS receivers and communication logs among mobile nodes. In more detail, it identifies the trajectories of mobile nodes from GPS logs in order to estimate pathways, and simultaneously estimates the presence of objects from the communication logs among mobile nodes in order to estimate obstacles. These results are finally merged to output an estimated entire map.
Our aim is to clarify the challenges in this automated generation of local maps and provide an efficient and reasonable approach. We need to take into account that GPS errors and uncertainty of radio propagation with presence of obstacles may have a negative effect on the map accuracy. To cope with this problem, we conducted several preliminary field experiments. Based on the results, we take an approach to using probabilities and counters to determine whether each subregion is occupied by an obstacle or not. After estimating the rough form of the obstacles, image-processing techniques are applied to improve the readability of the map. Two field experiments and several simulation experiments were conducted to validate the effectiveness of the algorithm. In particular, in the field experiments, we estimated the map of a 150 m×190 m region on our university campus and that of a 225 m×250 m region with many apartment buildings. The results from those experiments have shown that maps with about 85% accuracy were generated within a few hundred seconds.
Compared with our preliminary work that was presented in [3], this paper has considerable extensions along with new experimental results. They are summarized as follows. (i) We have conducted additional simulations and field experiments. In particular, a new field experiment was conducted in a region with many buildings. From the experimental results, we have confirmed that our method could recognize all the buildings and pathways with sufficient accuracy. These results are presented in Sections 4 Simulation experiments, 5 Field experiments. (ii) We have designed several extensions to the basic algorithm to enhance its capability. First, we have implemented a function to enable combined use of existing and generated maps that facilitates situation recognition. Second, we have addressed our ideas to deal with plausible and realistic situations that had not been considered in the basic algorithm. The details of the extensions are explained in Section 6. (iii) In order to motivate our work, we introduce a known map estimation technique called SLAM that simultaneously estimates the location and map of mobile robots. Then we explain the difficulty of applying SLAM to our case. This is introduced in Section 7.
The rest of this paper is organized as follows. Section 2 outlines the problem and design of the proposed algorithm, and Section 3 gives the algorithm description. Section 4 explains the simulation results, which are followed by the results from the two field experiments in Section 5. In Section 6, we give discussions on the design of possible algorithm extensions. Section 7 summarizes the related work and addresses the contribution of this paper. Finally we conclude this paper in Section 8.
Section snippets
Problem statement
In Fig. 1, we exemplify the environment in which our proposed algorithm works. A targeted region consists of movable space such as pathways, and obstacles such as buildings. We assume that a mobile node (or simply a node) is a person who has a wireless terminal and can move only in movable space. Each node has a GPS receiver and measures its current position every seconds. This position information contains some error range, which is unknown in the algorithm. It also has a mid-range
Algorithm description
The algorithm divides a targeted area into square cells, and estimates for each cell whether it is occupied by an obstacle or not. Hereafter, a cell occupied by an obstacle is called an obstacle cell and one in movable space is called a non-obstacle cell. A cell at row and column is denoted by .
Simulation experiments
We evaluated the performance of the proposed algorithm by simulations using the QualNet simulator [9] and Wireless InSite module [10] that can accurately simulate radio propagation based on several models. In order to test the performance in such situations that radio propagation is interrupted by obstacles such as buildings and the mobility of nodes is restricted, we used a map shown in Fig. 9 that models a 150 m×190 m region on our university campus (a picture of the region is shown in Fig. 10
Field experiments
We conducted two field experiments to evaluate the performance of our method in a real environment. One experiment was conducted to observe the performance difference between real and simulated environments. To this goal, we conducted the field experiment in the same situation as the simulation scenario of Section 4, where a part of our university campus was targeted. Another experiment was conducted to verify the algorithm performance in regions with many buildings.
Combined use of existing and estimated maps
Although existing maps or satellite images may not present the latest geography nor detailed structure of buildings, they are helpful in enhancing the performance of our algorithm. We have implemented a prototype system to display an estimated map overlaid on an existing image like Fig. 25, which can increase the readability of the estimated map. Furthermore, we may use existing maps in the rectangular approximation phase of the algorithm. More concretely, we can use the information about the
Related work and contribution
In this section, we introduce existing techniques for map generation, and explain why it is difficult to use them in our case.
Conclusion
We have proposed an algorithm to estimate the shapes and positions of obstacles using mobile nodes’ ad hoc communication devices and GPS receivers. The proposed algorithm estimates movable space and obstacles using GPS logs and communication logs, and refines the result by applying some image-processing procedures to obtain a readable map. Through several experiments in simulations and real environments, we have shown that our algorithm could generate readable and accurate maps.
As we stated in
Acknowledgement
This work was supported in part by Japan Science and Technology, CREST.
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2013, Nuclear Engineering and DesignCitation Excerpt :Such added awareness, on-the-go, of the surrounding environment would inherently provide the much needed localization information within the environment together with obstacle evasion (under the current circumstance radiation evasion) tactics that would lead the worker on the ground to safe exit through safe path planning. Several solutions were proposed providing localization for outdoor navigation as in GPS or differential GPS-based systems (Minamimoto et al., 2010; Meyer and Filliat, 2003; Motlagh et al., 2009); these systems, however, would not work properly for indoor environments due to limited GPS signal availability (Fuchs et al., 2011). Several solutions for indoor localization and safe path planning for avoiding any critical obstacles have been proposed for intelligent robot navigations (Meyer and Filliat, 2003; Motlagh et al., 2009; Jaradat et al., 2011).
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