Omnidirectional Pedestrian Navigation for First Responders

Abstract

It might be assumed that dead reckoning approaches to pedestrian navigation could address the needs of first responders, including fire fighters. However, conventional PDR approaches with body-mounted motion sensors can fail when used with the irregular walking patterns typical of urban search and rescue missions. In this paper, a technique using shoe-mounted sensors and inertial mechanization equations to directly calculate the displacement of the feet between footfalls is described. Zero-velocity updates (ZUPTs) at foot standstills limit the drift that would otherwise occur with inexpensive IMUs. We show that the technique is fairly accurate in terms of distance travelled and can handle many arbitrary manoevers, such as tight turns, side/back stepping and stair climbing.

RescueMe: An Indoor Mobile Augmented-Reality Evacuation System by Personalized Pedometry

“…Such techniques are limited to the sensor placement and the sensor quality. The most accurate results require external sensor placement in the users shoes [8], [13], [3], e.g. yielding 0.5 m -0.75 m accuracies in 8725 m2 of 3-story building space [13].…”

“…Dynamic stride estimation has been explored, but sensors are normally mounted on the foot in order to capture leg swing. Work to compensate for these errors employ the Kalman filter [8], [12], [11], Weinberg expression [10], or zero-velocity updates (ZUPT) [8], [3]. These three techniques minimize inertial drift and can predict actions based on prior event knowledge.…”

Point of Interest Awareness Using Indoor Positioning With a Mobile Phone

“…This approach is then enhanced by the use of surveillance cameras to estimate the walking parameters. Stéphane Beauregard (2008) focuses on the indoor positioning of emergency first responders. Shoe mounted sensors are used and a foot-inertial pedestrian dead reckoning approach is proposed to detect the displacement made by the foot in each footstep and consequently the total displacement made by the user.…”

Cascaded estimation architecture for integration of foot-mounted inertial sensors

“…Rest phases of the foot, which are detected from the accelerometer signals, trigger zero-velocity (virtual) measurements that are used to update the filter (ZUPT). Due to the regular ZUPT measurements we can estimate and correct the drift errors, which accumulate in the strapdown solution [5], [6], [7], [8]. It was shown in [5] that this approach can achieve very good performance even with today's low-cost micro-electro-mechanical (MEMS) sensors, because the ZUPTs are so frequent that errors build up only slowly during each step the pedestrian makes.…”