Efficient End-to-end Visual Localization for Autonomous Driving with Decoupled BEV Neural Matching
Journal:
arXiv
Published Date:
Mar 2, 2025
Abstract
Accurate localization plays an important role in high-level autonomous
driving systems. Conventional map matching-based localization methods solve the
poses by explicitly matching map elements with sensor observations, generally
sensitive to perception noise, therefore requiring costly hyper-parameter
tuning. In this paper, we propose an end-to-end localization neural network
which directly estimates vehicle poses from surrounding images, without
explicitly matching perception results with HD maps. To ensure efficiency and
interpretability, a decoupled BEV neural matching-based pose solver is
proposed, which estimates poses in a differentiable sampling-based matching
module. Moreover, the sampling space is hugely reduced by decoupling the
feature representation affected by each DoF of poses. The experimental results
demonstrate that the proposed network is capable of performing decimeter level
localization with mean absolute errors of 0.19m, 0.13m and 0.39 degree in
longitudinal, lateral position and yaw angle while exhibiting a 68.8% reduction
in inference memory usage.
