Chương 1 Introduction 1 Overview and Motivation According to the World Health Organization (WHO), "Disaster is an occurrence disrupting the normal conditions of existence and causing a level of suffering that exceeds the capacity of adjustment of the affected community” [Ll]. Disasters around the world including natural and man-made hazard have been damaging to communities and countries. They can be classified into two main types in Table In the case of natural disasters, such as the well-known Hur- Type Disasters Natural | Earthquakes, typhoons, hurricanes (storm), cyclone, volcanic eruption Man-made | Fire, Explosion, collision, shipwreck, structural col- lapse, environmental pollution.1: The taxonomy of disaster ricane Katrina in 2005 and the Haiti earthquake in 2010, the affected victims and areas were sizeable so that the rescue required worldwide and long-term action. However, the minor to moderate natural disasters may bring slight damage to a building and hurt people inside.
In the case of man-made disasters, fire in a building is the most frequent disaster.[| shows the trends in fires, deaths, injuries and dollar loss from U. of fire statistic from 2008 to 2017.3 million fires are reported and they cause more than 18000 casualties every year [2]. Though large disasters impact society more, small to moderate disasters are more frequent and likely to occur in our lives. In the case of small-scale disasters, people are apt to get trapped inside buildings.
For exam- ple, a moderate earthquake may cause a wall to collapse or a column of the structure or knock over furniture like bookcases. Consequently, victims might be trapped in the building, and get injured or even die if they do not get help to escape. In the case of fires in a building, most vic- tims die from smoke or toxic gases and not from burns [3]]. Therefore, the rapid rescue of people inside the building is the key to reducing casualties in the case of small to moderate disasters.
Considering the effects of the disaster in residential buildings in urban areas. An Indoor Positioning System (IPS) can greatly assist the victims and rescuers in case of disasters: 1. In Emergency Rescue Evacuation Support System by locating individuals inside the build- ing and guiding them to a safer place [/4]. Localizing the position of the victims, it may be include estimating emergency situations.
1 Trend me mũ | Hơn | sọ” Fires eer -6.2 % * Fires 2008-2017 PEN from 2008 Ly 1451.0% + Trend Actual in 2017 * from 2008 Hình 1.1: Trends in fires, deaths, injuries and dollar loss from U. fire statistic from 2008 to 2017 [2] 3. Supporting for the rescuers to identify the location of the victim and reach their position However, the use of IPS in disaster-relief is known to have challenges. That means the configuration of the Wireless systems (e.
WiFi access points, Bluetooth Beacons) are not changed over time. In addition, the positions of the landmarks (such as the map of the building, doors, elevators) are known beforehand by the system. e Semi-structured Environment: in this type, it is assumed that parts of the building are changed by disasters. Some of the wireless networks are not worked.
e Unstructured or Unknown Environment: in this type of scenario, the IPS does not have control over the environment. The electrical system may not work. The structure of the building can change and the wave propagation conditions are affected. This is the most challenging environment for the localization process, since the IPS has to deal with dy- namic changes of the environment.
Recently, different technologies are applied for these location services. Nevertheless, to have an IPS that can assist in finding and rescuing victims is still a big challenge for the environ- ments mentioned above. Since the GPS cannot guarantee location service because a line-of- sight transmission between receivers and satellites is not possible in an indoor environment, many approaches in current positioning technologies have been proposed: including Ultra Wide Band (UWB) [6], Ultrasound [7], Radio Frequency Identification tags (RFID) [8], Bluetooth Low Energy (BLE) [9], WiFi [I0], PDR using inertial sensors [1| {12} [[3],and Camera [14]. Among them, WiFi and BLE technology based on the Received Signal Strength (RSS) have become the common solutions for indoor positioning because of the convenience of measuring this value directly in most devices such as smart-phones, laptops.
In order to estimate a position from RSS, path-loss model-based or fingerprinting based approaches can be used. Firstly, a “path-loss” based approach is a technique that converts the values of RSS from the access points to the mobile receiver into distances based on a signal propagation model [5]. However, the position relationship is highly complex due to multi-path, 2 metal reflection and interference noise [[6ÏJ. Thus, the path-loss model may not be adequately captured by an invariant model.
Secondly, fingerprinting/sense analysis is a technique that esti- mates the position based on a scene analysis. This technique estimates the user’s position with regards to the similarities between the RSS measurements of online phase and RSS of the offline phase training [[[7I ([8/]. The main advantages of WiFi fingerprinting are that it takes advantage of current WiFi infrastructures, and the location of the access points can be unknown. On the other hand, the disadvantages of the fingerprinting method include the need for dense training coverage and the poor extrapolation of areas not covered during the training phase.
During the offline phase, it can be extremely time-consuming and labour-intensive to build substantially large fingerprinting databases [18]. Similar to WiFi, RSS of BLE technology can be used to locate an object. It is also affected by indoor environments. However, it has advantages of bi-transmission range, low energy con- sumption and getting information about the location in a few milliseconds [19].
Hence, the IPS using BLE can use path-loss model-based to avoid time-consuming of the training phase, but it can get high accuracy by different filters for fast response RSS measurements. Another widely adopted localization approach is PDR [Li] [13], which leverages inertial sensors to estimate the displacement of pedestrians relatively to their previous position. The main challenge in this approach is that the inertial sensors in commercial smart-phones often suffer from imperfect calibration and noisy measurements [[[8]]. In addition, step counting is currently a major method to capture the walking path and the movement of pedestrians [11].
The estimated location of PDR is often drifted when travelling a long distance due to inaccurate measurement of step detection, step length and heading. In the urban areas, the most challenging conditions such as darkness, power outages, high temperatures, smoke, flames, and noise can prevent an IPS from working. There are many dif- ferent ways to support rescuers and communication between rescuers and victims. However, taking into account the human in the building, an Indoor positioning system plays a critical role to minimize the damage of disasters, especially for evacuation by locating individuals inside the building and guiding them to safety.
The IPS should be convenient for everyone to use and highly accurate in structured environments. Another important mission in disaster relief 1s Search and Rescue (SAR). Rescue teams have to explore a large terrain within a short amount of time in order to locate survivors after a disaster. Simultaneous Localization and Mapping (SLAM) for robotics is very important to explore the environment autonomously or partially guided by the incident commander.
Their tasks are to jointly create a map of the terrain and to register victim locations, which can further be utilized by human task forces for rescue. In unstructured environments, a dynamic map from the robot can be useful for the indoor positioning system. It is clear that the IPS cannot work when all infrastructures of the building were collapsed. In this thesis, an indoor position framework is proposed with a partial infrastructure, which refers to the case where only a part of the infrastructure provides its functionality like electricity.
In addition, a ROBOT using SLAM techniques is also presented in this thesis to draw the building maps for both indoor positioning system and SAR. moreover, with the rescue robot, it provides promising solution to assist people in urban areas in term of: 1) reducing personal risk to people and rescue dogs by entering unstable structures, 2) increasing speed of response by penetrating ordinarily inaccessible voids, and 3) through the information from cameras and sensor fusion in order to extend the reach of rescuers to regions that are otherwise inaccessible [20]. Building Map Accelerometer Gyroscope (Environment Map) Robot Map ¡ "— Pedestrian Dead Reckoning | 1 Ej - Bert | 7m nh „ Posistion Estimation | > WiFi Fingerprinting (xy)1 || (RSSa, .RSSN)1 | BLE Beacon fingerprint r database : ( Hình 1.2: The general scheme of Indoor Positioning System 2 Research Questions For disaster relief, In order to reduce the damages and impacts on the human being in the buildings. There are two main research questions that need to solve in this work: e How to build an indoor positioning system for human in emergency rescue evacuation with partial infrastructures of the buildings? e How to deploy a robot using SLAM to build a dynamic map of the buildings for indoor positioning system and SAR? 3 Approach 3.1 Indoor Positioning System The general scheme of the proposed framework shows in Figure|[.2| The IPS deploys differ- ent sensors on smart-phone to build an effective indoor positioning system that can work in dif- ferent previous mentioned indoor environments.
Especially, the proposed framework focuses on fusing different indoor techniques (Fingerprinting, path-loss, PDR) and different technologies (WiFi, BLE, inertial sensors) and building map to improve the position accuracy and efficiency as the infrastructure becomes collapsed gradually. This IPS is low-cost, easy to integrate into 4 ~—=« ( 1 | | Sparse Training Fingerprinting Data _ P| | Opitmize the Parameters for Kernel (Gaussian | Genarated WiFi Map Offline : Proccess ) i | ow a a al el eal cl cal al cal al cal ll a al cal li ca al alt al al cal lt cal al cal i cal al cal st cal lf alt Ee ee ee ee al a —_ ` , WiFi % , (RSS) WiFi Position l | > ị | Ị | | Gyroscope - | Heading | b \ i ' Z L ” , ` > Ị ! : f Acelerometer A Position & b — Le Step Detection = [ | Step Length —> anes ee (Trajectory) Ũ I —ssss=ễ=ễẳẳx \ J i ————— ` b k = i Ũ | BI | ‘Model L b £ i k 1 L Path-loss Map of i Building _— Hinh 1.3: The hybrid framework of indoor positioning system in the building for disaster relief the building and convenient for people inside the building. It also can combine with robot to use dynamic map (robot map) when the environments change. Furthermore, In the offline phase of WiFi fingerprinting techniques, robot can be used to collected WiFi RSS at the reference points to reduce training time.
The general scheme of IPS in the building for disaster relief as shown in Eigure |[.3| The proposed framework aims at achieving location robustness and accuracy for disaster relief by combining a number of techniques: e Rss based path-loss for BLE iBeacon: the BLE beacons are deployed by RSS based using path-loss model to measure the distance between mobile users and these beacons. The noise of RSS measurement can be reduced by Kalman filter (KF). The benefit of BLE beacon is low energy consumption. So, it can work in blackout condition.
e Constructing a "WiFi map". With the aim of reducing the time needed for data training during the offline phase and for improving the accuracy of WiFi fingerprinting, a Gaus- sian process (GP) regression is employed. This makes it possible to obtain the mean and variance of the considered WiFi map based on the correlation between RSS of sparse training points.