Abstract
The localization of persons in indoor environments is nowadays an open problem. There are partial solutions based on the deployment of a network of sensors (Local Positioning Systems or LPS). Other solutions only require the installation of an inertial sensor on the person’s body (Pedestrian Dead-Reckoning or PDR). PDR solutions integrate the signals coming from an Inertial Measurement Unit (IMU), which usually contains 3 accelerometers and 3 gyroscopes. The main problem of PDR is the accumulation of positioning errors due to the drift caused by the noise in the sensors. This paper presents a PDR solution that incorporates a drift correction method based on detecting the access ramps usually found in buildings. The ramp correction method is implemented over a PDR framework that uses an Inertial Navigation algorithm (INS) and an IMU attached to the person’s foot. Unlike other approaches that use external sensors to correct the drift error, we only use one IMU on the foot. To detect a ramp, the slope of the terrain on which the user is walking, and the change in height sensed when moving forward, are estimated from the IMU. After detection, the ramp is checked for association with one of the existing in a database. For each associated ramp, a position correction is fed into the Kalman Filter in order to refine the INS-PDR solution. Drift-free localization is achieved with positioning errors below 2 meters for 1,000-meter-long routes in a building with a few ramps.
Highlights
The location of people in outdoor environments is in general possible with a GPS receiver
Some PDR solutions basically integrate the signals coming from an Inertial Measurement Unit (IMU), which generally includes three accelerometers and three gyroscopes, and add special constraints for sensor error estimation [3]
While the foot is stationary, totally or at stance intervals, there are several measurements available (ZUPT, Zero Angular Rate Updates (ZARU) and Compass) that are used to update the estimations in the Extended Kalman Filter (EKF); while the foot is on the swing phase, instead of filtering, the EKF predicts errors
Summary
The location of people in outdoor environments is in general possible with a GPS receiver. It can be used to select a movement model in a PDR implementation (e.g., walking at a continuous pace), and even more importantly, action recognition can be used to get clues about where a person could be located, allowing to make position corrections to eliminate drift. This latter approach is the one that we exploit in this work. Apart from the actions already mentioned they include: standing in a lift, on a conveyor belt, on a sports treadmill, riding a bike, jumping, rowing, etc None of these works really apply action recognition to correct the drift in PDR, nor propose a method for ramp detection. In last section, we give the main conclusions drawn from this work
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