# An In-Vehicle Smartphone RTK/DR Positioning Method Combined with OSM Road Network
## 1 Introduction  
Vehicle navigation is an indispensable item in the rapid development of intelligent transportation and intelligent vehicles related to practical needs, such as fast wayfinding, avoiding traffic congestion, and improving traffic operation rate [1]. Following the widespread use of hardware sensors in intelligent vehicle systems, the method of vehicle positioning has also expanded from single GNSS (global navigation satellite systems) positioning to fusion positioning using GNSS receivers, inertial navigation systems [2], LIDAR (light detection and ranging) devices [3], and odometers [4, 5]. For most drivers, smartphones, as indispensable devices for travel, perform the task of vehicle navigation in most driving environments. Currently, some smartphone GNSS receivers can output pseudorange, Doppler, carrier phase, and other satellite observation data in real time at 1 Hz frequency. At the same time, the smartphone can receive observation data broadcast by the reference station through the network. By differencing the decoded reference station observation data and satellite observation data, RTK positioning of the smartphone can be achieved [6, 7, 8]. At present, the RTK positioning of smartphones has been used in some models and can play a stable role in an open-air environment. However, in the complex urban canyon environment, the interference of errors, such as multipath and non-line-of-sight [9, 10] make it challenging to guarantee smartphone positioning accuracy and stability. The inertial sensor integrated inside the smartphone provides the possibility for dead reckoning (DR) [11, 12, 13, 14]. In practicalapplications, the low-cost inertial navigation equipment has relatively high data noise and the short-time integral operation introduces significant errors in velocity, attitude, and position [15; 16]. Therefore, the RTK/DR method result will change with the RTK result, and it is not easy to effectively improve the positioning accuracy. Therefore, improving DR accuracy becomes the critical point in order to solve the problem. Velocity and heading angle are the main factors affecting DR. Since the accelerometer noise of the smartphone is relatively large, the velocity obtained by RTK calculation is more reliable than updating velocity through the acceleration integral. Similarly, the heading angle error accumulates through the angular velocity, and acceleration integral update is fast, in addition to being needed to correct the heading angle in real time. The correction of the heading angle is generally based on the information of vehicle motion state and road environment. There is a range of available methods, e.g., zero velocity correction [17], road line constraints [18], straight line detection [19], and map matching [20]. Considering the in-vehicle navigation of smartphones, positioning is often used in conjunction with maps so that it is more practical to correct the heading angle through the road direction. According to the reference [21], the currently available open-source OSM (OpenStreetMap) road network data have an overall directional similarity of 95% and an accuracy level sufficient to correct the heading angle. Therefore, this paper proposes a method for computing the heading angle using the OSM road direction and designs a point-to-line map matching scheme, which combines the vehicle motion state and the change of the DR heading angle to select an appropriate timing for correction. Smartphone RTK uses a robust Kalman filter to adapt to complex urban environments. RTK/DR uses a combination method and estimates velocity and position based on the standard Kalman filter. After heading angle correction, DR has high directional accuracy. However, when RTK seriously jumps, the error affecting RTK/DR combination will cause the overall offset of subsequent DR.