Robust Localization Based on Kernel Density Estimation in Dynamic Diverse City Scenes Using Lidar

Rendong, 王任栋 Wang; Hua, 李华 Li; Kai, 赵凯 Zhao; Youchun, 徐友春 Xu

doi:10.3788/aos201939.0528003

Cited by 2 publications

(1 citation statement)

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“…Secondly, in the localization update phase, the mean and variance of pose are updated by using the difference between the real observation data and the predicted observation data as well as the matching degree as the localization output. In [14,15], the inconsistency between high-precision point cloud map data and point cloud data collected in real time is expressed by probability. The authors converted the localization process into two steps, including prediction and a prediction update, to obtain a better prediction of the uncertainty of localization.…”

Section: Introductionmentioning

confidence: 99%

A Particle Shift Prior Information Fusion Localization Algorithm for the Autonomous Internet of Vehicles

Chen

Tang

et al. 2022

Electronics

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Ensuring that a vehicle can obtain its real location in a high-precision prebuilt map is one of the most important tasks of the Autonomous Internet of Vehicles (AIoV). In this work, we show that the resampling of the particle filter (PF) algorithm is optimized by using the prior information of particles that shift real localizations to improve vehicle localization accuracy without changing the existing PF process, i.e., the particle shift filter (PSF). The number of particles is critical to their convergence efficiency. We perform quantitative and qualitative analyses of how to improve particle localization accuracy while ensuring timeliness, without increasing the number of particles. Moreover, the cumulative error of the particles increases with time, and the localization accuracy and robustness decrease. Our findings show that the initial particle density is 159 particles/m3, and the cumulative variance of the PSF particles is improved by 27%, 29%, and 82% at the x-, y-, and z-axes, respectively, under the same conditions as the PF algorithm, while the calculation time only increases by 10.6%. Moreover, the cumulative mean error is reduced by 0.74 m, 0.88 m, and 0.68 m at the x-, y-, and z-axes, respectively, indicating that the localization error of the PSF changes less with time. All experiments were performed using open-source software and datasets.

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Section: Introductionmentioning

confidence: 99%