Figure 8 - available via license: Creative Commons Attribution 4.0 International
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Modular approach to designing a state estimator for odometry localization (Version 111 based on basic version, changes marked with dashed lines or dashed blocks and blue letters, respectively). Before introducing additional versions, the meaning of the version numbers is explained in Figure 9. There are three complementary models in total. The three numbers of the version number represent how the three complementary models have been used in the version, respectively. There are two states to describe the usage of model-1 and three states each for model-2 and model-3. Model-1 describes the relation between the vehicle
Source publication
Localization and navigation not only serve to provide positioning and route guidance information for users, but also are important inputs for vehicle control. This paper investigates the possibility of using odometry to estimate the position and orientation of a vehicle with a wheel individual steering system in omnidirectional parking maneuvers. V...
Citations
This paper presents a novel approach for dead reckoning. The described localization system consists of an inertial navigation system (INS), a magnetic, angular rate, gravity sensor (MARG sensor) and an odometry an odometry model. In contrast to conventional odometry models, a kinematic two-track model is implemented. The odometry model uses wheel-individual steering angles. It can therefore be applied for vehicles with all-wheel steering and steering geometries that allow opposite steering angle directions at one axle. An error-state Kalman filter is used to merge the individual submodels. While conventional odometry based localization systems only consider the vehicle longitudinal and lateral speed and position change, the proposed localization system is also able to correctly represent movements along the vehicles vertical axis. Furthermore, the algorithm uses the vehicle acceleration calculated from the odometry model to increase the robustness of the orientation estimation. The aim of the localization method is a high positioning accuracy in the low speed range, e.g. during precise maneuvering or parking. For this reason, the accuracy of the localization system is demonstrated by driving tests on a parking lot. The all-electric vehicle platform flexCAR is used as a test vehicle. Through its symmetrical design, this vehicle is able to realize wheel steering angles of up to 30° on the front and rear axles. Due to its maneuverability it is particularly suitable for the investigation of parking and (maneuvering).KeywordsVehicle-Self-LocalizationDead ReckoningMARGAHRSINSOdometryError-State Kalman Filter (ESKF)
