Abstract
The paper presents an algorithm for estimating a pedestrian location in an urban environment. The algorithm is based on the particle filter and uses different data sources: a GPS receiver, inertial sensors, probability maps and a stereo camera. Inertial sensors are used to estimate a relative displacement of a pedestrian. A gyroscope estimates a change in the heading direction. An accelerometer is used to count a pedestrian's steps and their lengths. The so-called probability maps help to limit GPS inaccuracy by imposing constraints on pedestrian kinematics, e.g., it is assumed that a pedestrian cannot cross buildings, fences etc. This limits position inaccuracy to ca. 10 m. Incorporation of depth estimates derived from a stereo camera that are compared to the 3D model of an environment has enabled further reduction of positioning errors. As a result, for 90% of the time, the algorithm is able to estimate a pedestrian location with an error smaller than 2 m, compared to an error of 6.5 m for a navigation based solely on GPS.
Highlights
GPS-NAVSTAR (Global Positioning System-NAVigation Signal Timing And Ranging), popularly known as the GPS system, has considerably gained in civilian interests, since May 2000
Outages in harsh environments like tunnels, underground passages, dense urban areas etc. Positioning data from these two sources are usually integrated by the Extended Kalman filter or particle filter, which perform well when errors can be modelled by white noise which has the property of being uncorrelated with itself
GPS location estimates are augmented by dead reckoning techniques derived from inertial sensors, i.e., gyroscopes and accelerometers
Summary
The particle filter is a sequential version of the Monte Carlo method [37] which enables to find solutions of a multidimensional problem by generating a large number of possible solutions, so-called particles, and verifying these solutions by a given criterion, i.e., an error function. The particle filter can be looked at from the statistical point of view, whereby the solution is represented by a probability density function. Wi (k) where xi (k) is a possible system state at time instant k and wi (k) is the weight associated with i-th particle. The aim of this simulation is to to find the most probable user location. Weights wi (k) of all generated particles should sum up to unity
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