Abstract
Investigators publish new report on Robotics Robotics and Automation. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “High-precision vehicle localization with commercial setups is a crucial technique for high-level autonomous driving tasks. As a newly emerged approach, monocular localization in LiDAR map achieves promising balance between cost and accuracy, but estimating pose by finding correspondences between such cross-modal sensor data is challenging, thereby damaging the localization accuracy.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Tsinghua University, “In this letter, we address the problem by proposing a novel Transformer-based neural network to register 2D images into 3D LiDAR map in an end-to-end manner. We first implicitly represent poses as high-dimensional feature vectors called pose queries and gradually optimize poses by interacting with the retrieved relevant information from cross-modal features using attention mechanism in a proposed POse Estimator Transformer (POET) module. Moreover, we apply a multiple hypotheses aggregation method that estimates the final poses by performing parallel optimization on multiple randomly initialized pose queries to reduce the network uncertainty.”
NETL
NSTL
IEEE