The accuracy analysis of iterative weighted IEKF-BP combination algorithm for LiDAR/Radar loose combination

doi:10.13474/j.cnki.11-2246.2021.0041

Abstract

Abstract: In order to verify the superiority of the multi-sensor combined positioning model in high-precision positioning, and to solve the problems of inconsistent prediction accuracy standards, untimely prediction, and high misprediction rates in autonomous positioning and navigation, this article proposes a novel LiDAR/Radar integrated positioning model based on iterative weighted IEKF-BP combination algorithm using its data, and the accuracy of the combined positioning results of the two sensors is analyzed. The experiment shows that the combined result accuracy of the iteratively weighted IEKF-BP combined algorithm is better than the combined positioning accuracy of the single IEKF algorithm and the BP neural network algorithm. Among them, the root mean square errors in the X and Y directions are 0.028 and 0.028 m. The average errors are 0.023 and 0.014 m. The result can accurately reflect the movement state of the carrier and meet the future positioning needs of unmanned driving.

Key words: integrated positioning and navigation, LiDAR/Radar loose combination positioning, IEKF, BP neural network, iterative weighted IEKF-BP combined positioning algorithm

CLC Number:

SONG Bao, KE Fuyang, ZHAO Xingwang. The accuracy analysis of iterative weighted IEKF-BP combination algorithm for LiDAR/Radar loose combination[J]. Bulletin of Surveying and Mapping, 2021, 0(2): 44-48.

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