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Real-time positioning in suburban and urban environments has been a challenging task for many Intelligent Transportation Systems (ITS) applications. In these environments, positioning using Global Navigation Satellite Systems (GNSS) cannot provide continuous solutions due to the blockage of signals in harsh scenarios. Consequently, it is intrinsic to have an independent positioning system capable of providing accurate and reliable positional solutions over GNSS outages. This study exploits the integration of Light Detection and Ranging (LiDAR), gyroscope, and odometer sensors, and a novel real-time algorithm is proposed for this integration. Real field data, collected by a moving land vehicle, is used to test the presented algorithm. Three simulated GNSS outages are introduced in the trajectory such that each outage lasts for five minutes. The results show that using the proposed algorithm can achieve a promising navigation performance in urban environments. In addition, it is shown that the denser environments, that existed over the second and third outages, can provide better positioning accuracies as more features are extracted. The horizontal errors over the first outage, with less density of surroundings, reached 7.74 m (0.43%) error with a mean value of 3.15 m. Moreover, the horizontal errors in the denser environments over the second and third outages reached 4.97 m (0.28%) and 3.99 m (0.23%), with mean values of 2.25 m and 1.89 m, respectively.

In this work, the accuracy of using Single Point Positioning (SPP) and Precise Point Positioning (PPP) techniques is assessed in case of using long observational periods. Positioning using Differential Global Navigation Satellite System (DGNSS) technique is adopted to achieve reliable accurate results to be used as a reference in the evaluation process for both SPP and PPP positioning techniques. A number of neighboring International GNSS Service (IGS) stations were participated in this study for the differential positioning. GNSS data over one month for a continuous operating station at Faculty of Engineering, Ain Shams University were collected and processed on a daily basis in the SPP and PPP processing modes. In addition, DGNSS processing sessions using the IGS stations were carried out for each day of the month. The positional differences for both SPP and PPP solutions with respect to the DGNSS solutions were computed. The results showed that the SPP solutions had positional differences with mean value 23.4cm, while the PPP solutions had positional differences with mean value 12.7cm. In addition, by considering a unique monthly solution for each technique, the positional difference reaches to 15 cm in case of the SPP solution and 12.5 cm in case of the PPP solution. Thus, applications that do not require immediate positioning, such as wildlife management and insect infestation, can benefit from these results with low-cost hardware components. In addition, monitoring significant tectonic motions caused by earthquakes is another application in this context.

Pedestrian and vehicular navigation relies mainly on Global Navigation Satellite System (GNSS). Even if different navigation systems are integrated, GNSS positioning remains the core of any navigation process as it is the only system capable of providing independent solutions. However, in harsh environments, especially urban ones, GNSS signals are confronted by many obstructions causing the satellite signals to reach the receivers through reflected paths. These No-Line of Sight (NLOS) signals can affect the positioning accuracy significantly. This contribution proposes a new algorithm to detect and exclude these NLOS signals using 3D building models constructed from Volunteered Geographic Information (VGI). OpenStreetMap (OSM) and Google Earth (GE) data are combined to build the 3D models incorporated with GNSS signals in the algorithm. Real field data are used for testing and validation of the presented algorithm and strategy. The accuracy improvement, after exclusion of the NLOS signals, is evaluated employing phase-smoothed code observations. The results show that applying the proposed algorithm can improve the horizontal positioning accuracy remarkably. This improvement reaches 10.72 m, and the Root Mean Square Error (RMSE) drops by 1.64 m (46 % improvement) throughout the epochs with detected NLOS satellites. In addition, the improvement is analyzed in the Along-Track (AT) and Cross-Track (CT) directions. It reaches 6.89 m in the AT direction with a drop of 1.076 m in the RMSE value, while it reaches 8.64 m with a drop of 1.239 m in the RMSE value in the CT direction.