## 6 Conclusions  
This paper describes a way to incorporate observations from multiple GNSS constellations and on multiple carrier frequencies as well as odometry measurements into a tightly coupled GNSS/IMU/odometry integration algorithm. An existing algorithm working with GPS L1 C/A code pseudo-ranges and time-differenced carrier-phase observations as the only GNSS data is upgraded to include code pseudo-range measurements on GPS L2C and GPS L5 in a first step. The pseudo-ranges on different carrier frequencies are used to form ionosphere-free linear combinations. These combinations directly cancel out most of the ionospheric error and can be processed without having to change the EKF's state vector. The increased code tracking noise of ionosphere-free combinations is filtered out due to the EKF's inherent smoothing capability and does not influence positioning performance negatively. Initially, the attempt to work with L2C-L1 C/A and L5-L1 C/A ionosphere-free combinations as well as single-frequency L1 C/A pseudo-ranges simultaneously did not accomplish the expected gain in accuracy. On the contrary, positioning performance became worse than for the basic single-frequency algorithm. Large DCBs, especially for the L2C-L1 C/A combinations, were suspected to be the cause of this behavior. A subsequent calibration of DCBs for GPS and Galileo verified this hypothesis and asserted the long-term stability of all relevant DCBs, enabling the utilization of a total of six different types of pseudo-range observations (three for GPS, three for Galileo) within the same integration filter. The inclusion of Galileo observations introduces two additional types of biases: the GGTO and an ISB. While the former could be compensated via information from one of the navigation messages, the latter proved to be too large to be ignored. As these two biases influence the observations in the same way, they can be subsumed into one bias term. To absorb the effects of this bias term, an additional state for the Galileo receiver clock bias was added to the EKF's state vector. In an effort to facilitate the correction of the navigation solution derived from IMU measurements even in situations without GNSS reception, odometry observations were introduced into the localization algorithm as additional source of information. For this purpose, an odometry model with five inputs (four angular wheel velocities and the steering wheel angle) was incorporated. This model outputs the 2-D horizontal velocity for each wheel and provides reliable velocity observations even for cornering and slipping vehicles. The evaluation of two data sets proves the advantages of including multi-frequency/multi-constellation GNSS observations and odometry measurements into the localization algorithm. Incorporation of Galileo satellites increases the number of simultaneously available measurements and reduces the PDOP. This is especially true in situations with limited GNSS availability, e.g. inside the urban canyons contained in the first data set. The utilization of ionosphere-free combinations reduces the RMS position error, especially during periods of favorable GNSS reception conditions. Due to the successful calibration of DCBs, the localization algorithm is capable of working with the maximum possible amount of ionosphere-free combinations at any time, reducing the impact of ionospheric errors to a minimum. In terms of odometry, the evaluation points out two main improvements accomplished by the odometry model: On the one hand, the inclusion of odometry measurements enhances the estimation accuracy of yaw angle and lateral velocity, primarily when the vehicle is cornering. On the other hand, odometry aiding is able to maintain the horizontal position accuracy achieved during periods of unimpaired GNSS reception throughout periods with poor or no GNSS reception. This reduces the growth of the horizontal position error over time and results e.g. in a significant decrease of the horizontal 95 % error quantile.