PDR with a Foot-Mounted IMU and Ramp Detection

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.

LookUp

“…Jimenez et al [34] use ramp detection in indoor environments to provide drift correction in indoor locations. Woodman et al [35] developed a tracking system that uses a foot-mounted inertial sensor, a model of a building, and a particle filter to track a pedestrian in an indoor environment.…”

Gradient Profiling for Pedestrian Services

“…Many car producers [5] are now integrating night vision, active breaking and automatic steering solutions in their new models to reduce pedestrian accidents. Honda is developing a Vehicle-to-Pedestrian technology that is able to detect a pedestrian with a DSRC enabled smartphone [6].…”

A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model

“…However, some of them require additional infrastructures such as access points (AP) and base stations. Thus, in this paper, we mainly concentrate on Personal Dead-reckoning (PDR), in which only the inertial sensor of the phone is used [6]. This relative localization method measures and tracks the momentary location and trajectory of a walking person dependently using the smartphone without any external sensors.…”

“…However, this is very complex and cumbersome, requiring the storage of a large amount of map information which leads to extremely high time complexity [13], because it only uses the trajectory calculated by PDR, regardless of some possible characteristic contexts during the moving process, such as stairs, elevators, ramps, detours, etc. In order to simplify the map matching algorithm, Jiménez et al used an inertial measurement unit (IMU) sensor fixed on a person’s body to detect the movement of going up a ramp [6], which combines the trajectory with the recognized features, so that saves the storage of map information. After detection, the ramp is checked for association with one of the positions stored in a database.…”