## 2 Related Work  
The topic of pedestrian navigation including navigation aids for blind pedestrians has been described in many publications [9; 10; 11]. The first step to correct GPS readouts is to apply inertial sensors. Inertial sensors are mainly used in aviation to compute the orientation and position of an aircraft. Calculating the location requires double integration of the acceleration vector. Prior to these calculations the gravity acceleration must be removed, as accelerometers cannot distinguish gravity from an aircraft's accelerations. Therefore, the orientation of an aircraft with respect to the Earth's surface must be calculated from gyroscopes. The technique is known as INS (Inertial Navigation Systems) and warrants very precise and expensive laser sensors using the Sagnac effect. However, the strict implementation of INS using MEMS sensors (Micro Electro-Mechanical Systems) is useless after few seconds due to errors growing quadratically with time [12]. A travelled distance of a pedestrian can be estimated with surprisingly good accuracy by measuring the length of steps. This is done by analysing the acceleration in the gravity axis [10; 13]. As a person walks, the body undulates according to the strides. The technique is accurate from 0.5% to 10%, depending on the gait style. ZUPT (Zero Velocity Update) technique exploits the fact that a foot is at rest for some short period. An accelerometer must be mounted to a foot which is an inconvenience, offset however by better accuracy compared to the previous method [14]. A heading direction can simply be read out from a magnetic compass, optionally supported by a gyroscope. A compass is sensitive to local distortions of a magnetic field due to cars, power lines _etc._[10]. An electric tram can compromise a compass readouts within the radius of up to 100 m. A gyroscope, coupled by the Kalman filter, can reduce erroneous readouts [15]. The combination of GPS and inertial sensors readouts provides continuous estimates during GPS 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. However, the GPS errors are characterized by coloured noise [2; 16]. This is because when a GPS receiver loses track of satellites its position is estimated by using the history of previous locations. Secondly, signals from satellites occluded by the same building are equally delayed, which introduces a bias in a given direction. This feature of errors corrupting GPS readouts was reported in earlier studies [14; 17]. [PERSON] _et al._[18] present an interesting concept where the height of buildings were utilized to check if a given satellite is occluded. The authors carried out simulation which yielded good results. To improve the accuracy of dead-reckoning techniques, an additional source of the absolute pedestrian location should be introduced. Experiments in [4] showed that a combination of GPS and GLONASS navigation systems reduces root-mean-square (RMS) error by half and outliers by several times. Another solution is introducing the so-called probability map as a new source of data, which defines regions of a terrain that the user is most likely to enter or cross. A pedestrian is more likely to walk along pavements, parking places, paths, _etc._, rather than crossing walls, water ponds, _etc._ This cuts down the positioning errors as they start to aggregate. This map-based navigation concept proved its usefulness in outdoor pedestrian applications [19, 20] as well as indoor positioning [21, 22, 23]. In built-up areas, a probability map limits inaccuracy to a width of an urban canyon, usually delineated by buildings on both sides. A further improvement of positioning accuracy requires positioning with respect to known landmarks, _i.e._, the so-called exteroception.