Non-GPS Navigation for Emergency Responders

Abstract - This paper introduces a novel navigation system for walking persons. The system is of particular benefit for emergency responders, who often have to enter and move around in large structures where GPS is unavailable. We refer to our system as “Personal Odometry System” (POS). The POS measures the position of a walking person relative to a known starting position, such as the entrance to a building. This is accomplished by instrumenting one boot of the subject with a 3-axis gyroscope and a 3-axis accelerometer (collectively called “inertial measurement unit” – IMU). This paper describes the POS hardware and explains the basics of our approach. The paper also presents extensive experimental results, which illustrate the utility and practicality of our system. I. I NTRODUCTION This paper describes our Personal Odometry System (POS) for measuring and tracking the momentary location and trajectory of a walking person, even if GPS is not available. We believe that such a system might be of particular value for emergency responders. For example, fire fighters entering a burning building are at risk to be injured and unable to report their position. With the POS reporting the user’s position to a central command post, each emergency responder’s location could be tracked in real-time. Another application involves the “clearing” of a large building by emergency or security personnel. Their challenge often is to keep track of rooms already cleared and areas that were not cleared, yet. Our system’s ability to track each person’s location provides a useful solution for this problem. Our proposed POS does not require GPS. This is an important distinction, since GPS is not available indoors. Furthermore, GPS is unreliable under dense foliage, in so-called “urban canyons,” and generally in any environment, in which a clear view of a good part of the sky is not available. There are some approaches to personal position estimation without GPS. Typically, these systems require external references, also called “fiducials,” such as preinstalled active beacons, receivers, or optical retroreflectors. Common to all fiducial-based position estimation systems is that the fiducials must be installed in the work space at precisely surveyed locations before the system can be used. This installation is time consuming and expensive, and in case of emergency response completely unfeasible. Fiducial-based systems also require an active radiation source, such as infrared light [1], ultrasound [2], or magnetic fields [3], which may be undesirable in security-related applications. Generally, fiducial-based systems perform well and are able to provide absolute position and orientation in real-time. If the application permits the installation of fiducials ahead of time, then these systems have the significant advantage that errors don’t grow with time, as is the case in our POS. Another way of implementing absolute position estimation is computer vision ([4] and [5]). Images are compared and matched against a pre-compiled database. Computer vision has the advantage that the environment does not need to be modified, but the approach requires potentially very large databases. Work is also being done on so-called Simultaneous Location and Mapping (SLAM) methods, which don’t require a precompiled database. However, SLAM systems are not as reliable, may accrue errors over time and distance, and poor visibility and unfavorable light conditions can result in completely false position estimation [6][7]. The scientific literature offers only very few approaches that do not require external references. The simplest one of them is the pedometer, that is, a device that counts steps. Pedometers must be calibrated for the stride length of the user and they produce large errors when the user moves in any other way than his or her normal walking pattern. One commercially available personal navigation system based on this principle is the Dead Reckoning Module (DRM) by PointResearch [8]. It uses accelerometers to identify steps, and linear displacement is computed assuming that the step size is constant. Orientation is measured using a digital compass, which is combined with the traveled distance (step counts) to estimate 2-D position. The performance of this system depends on the accuracy of determining the stride