Improvement of Reliability Determination Performance of Real Time Kinematic Solutions Using Height Trajectory

Abstract

Autonomous driving support systems and self-driving cars require the determination of reliable vehicle positions with high accuracy. The real time kinematic (RTK) algorithm with global navigation satellite system (GNSS) is generally employed to obtain highly accurate position information. Because RTK can estimate the fix solution, which is a centimeter-level positioning solution, it is also used as an indicator of the position reliability. However, in urban areas, the degradation of the GNSS signal environment poses a challenge. Multipath noise caused by surrounding tall buildings degrades the positioning accuracy. This leads to large errors in the fix solution, which is used as a measure of reliability. We propose a novel position reliability estimation method by considering two factors; one is that GNSS errors are more likely to occur in the height than in the plane direction; the other is that the height variation of the actual vehicle travel path is small compared to the amount of movement in the horizontal directions. Based on these considerations, we proposed a method to detect a reliable fix solution by estimating the height variation during driving. To verify the effectiveness of the proposed method, an evaluation test was conducted in an urban area of Tokyo. According to the evaluation test, a reliability judgment rate of 99% was achieved in an urban environment, and a plane accuracy of less than 0.3 m in RMS was achieved. The results indicate that the accuracy of the proposed method is higher than that of the conventional fix solution, demonstratingits effectiveness.

Utilizing Probabilistic Maps and Unscented-Kalman-Filtering-Based Sensor Fusion for Real-Time Monte Carlo Localization

“…Satellite-based localization has been available for decades and has undergone various upgrades. Newer systems, such as RTK-GPS [23] or DGPS [24], provide efficient options since they attain centimeter-level precision without the use of any extra techniques. Nonetheless, there are significant uncertainties concerning their consistency.…”

“…For each particle (out of the 𝑀 randomly generated particles at the initiation phase of the procedure), two values are calculated for each particle (which represents a possible egocar); the first one is the state hypothesis (𝑥 [ ] ), and the second one is the state transition distribution 𝑝 𝑥 |𝑢 , 𝑥 [ ] , which is calculated using the object's motion model explained in Section 5. A weight is created for each particle based on its generated state hypothesis (importance) among the other state hypotheses of the other particles, as given in Equation (23). Each weight takes the form of a multivariate Gaussian probability density function that is computed for each observation and the likelihood of all the observations are combined by taking their multiplication product.…”

“…[m] t−1 , which is calculated using the object's motion model explained in Section 5. A weight is created for each particle based on its generated state hypothesis (importance) among the other state hypotheses of the other particles, as given in Equation (23). Each weight takes the form of a multivariate Gaussian probability density function that is computed for each observation and the likelihood of all the observations are combined by taking their multiplication product.…”