## 2 Related Work
The accuracy of landmarks' location should be approximately an order of magnitude better than the target accuracy of _ca._ 2 m. A vision-based technique called SLAM (Simultaneous Localization and Mapping) enables to build a map of the surrounding space and provide localization at the same time without employing any global navigation method. The SLAM based technique can be applied to build a 3D map of the environment. A review of SLAM techniques, modifications, results, _etc._, are given in book [24]. An application of SLAM for pedestrian navigation with results is given in [25]. Also a navigation system for the blind using a stereo-camera for tracing landmarks is presented in [26]. On the other hand, precise maps of outdoor or indoor environments may already be available, e.g., a plan of a building or plans of a city which are accurate to single centimetres, accuracy hardly achievable by any SLAM techniques. There are different sensors used to compare the environmental map with a robot's or pedestrian's location. Ultrasound sensors measure distance with 1% accuracy, being at the same time very cheap and compact. The maximum achievable distance is _ca._ 10 m. The angular resolution, however, is poor, \(\pm 15^{\circ}\). Laser scanners provide 0.1% distance measurement accuracy with a maximum distance of _ca._ 50 m. The angular resolution is of a fraction of a degree. The scanning is omnidirectional. The power consumption is _ca._ 10 W for longer distances. The size of a laser sensor is around 5 cm by 5 cm by 5 cm. The cost is rather high (_ca._ $4,000 USD depending on accuracy, maximum distance _etc._). These sensors are commonly used in robotics. Time-of-Flight (ToF) cameras offer similar parameters as laser sensors providing 3D reconstruction of the scanned environment. The angle of view is _ca._\(30^{\circ}\). Application of laser scanners and ToF cameras is limited to indoor environments due sunlight interference. In [27] ultrasound and laser sensing techniques are compared for indoor positioning of a robot. Work [23] presents an indoor localization system based on the particle filter. The user wears ultrasound sensors which measure a distance to adjacent walls. Therefore, the user location can be compared against the alignment of building walls. Application of an ultrasound sensor improved the positioning accuracy by 7 times in good trial conditions, _i.e._, with no additional objects in the building's corridor, closed doors _etc._ A similar technique using a laser sensor mounted on a white cane is reported in [22]. A laser sensor detects corners of corridors what provides a comparison with a building's plan. Finally, stereovision is a passive imaging technique that can be used indoors and outdoors. The cameras can be compact and inexpensive. However, stereovision imaging is limited to the environments featuring good lighting conditions, requires calibration of the stereovision optics and offers worse depth estimation accuracy than the earlier outlined active methods [28]. Moreover, demanding computations are required for calculating depth maps. In stereovision, depth accuracy can range from a few centimetres for objects located in a few metres range to a few metres for more distant objects. The so-called subpixel interpolation methods were proposed to improve depth estimation accuracy [29]. The work presented here combines global and local positioning techniques. GPS location estimates are augmented by dead reckoning techniques derived from inertial sensors, _i.e._, gyroscopes and accelerometers. A stereo-camera is used for distance measurements. Finally, a digital map of the outdoor terrain is incorporated into the algorithm. Each of the listed source of positioning data features different error characteristic, e.g., in open spaces GPS readouts are most accurate whereas stereovision may yield large depth estimation errors. Conversely, for city canyons where GPS accuracy filters, stereovision and the digital map offer good positioning. A particle filtering algorithm is proposed to optimally fuse all data source.