Abstract
A high-performance differential global positioning system (GPS) receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.
Highlights
Autonomous navigation is one of the most key technologies for driverless cars
Along the ground truths of autonomously driving trajectories, we investigated position errors for stand-alone global positioning system (GPS)–inertial measurement unit (IMU), dead reckoning (DR) and our data fusion method
InInthis data fusion fusion method methodfor foraccurate accuratenavigation navigationofof driverless cars based on a set of predictive models and occupancy grid constraints
Summary
Autonomous navigation is one of the most key technologies for driverless cars. Accurate positioning and orientation estimation of vehicles is generally regarded as the basis of many sophisticated modules such as environmental perception, path planning, and autonomous decision-making of driverless cars under complex urban scenarios. Two widely used multipath mitigation methods, i.e., high-resolution correlator (HRC) and multipath mitigation technique (MMT), and a new coupled amplitude delay lock loops (CADLL) method, wich is based on multipath signal amplitude, code phase, and carrier phase, are evaluated in [3]. They may fail under dynamic multipath scenario or when multipath is stronger than line-of-sight (LOS). Except for GPS, DR that employs vehicle kinematic model and incremental measurements of wheel encoder is often viewed to play a crucial role in precise short-term navigation of driverless cars [4]. Substantial efforts have been made to improve long-term precision and robustness through slip estimation [6]
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