Neural Error Covariance Estimation for Precise LiDAR Localization
Journal:
arXiv
Published Date:
Jan 5, 2025
Abstract
Autonomous vehicles have gained significant attention due to technological
advancements and their potential to transform transportation. A critical
challenge in this domain is precise localization, particularly in LiDAR-based
map matching, which is prone to errors due to degeneracy in the data. Most
sensor fusion techniques, such as the Kalman filter, rely on accurate error
covariance estimates for each sensor to improve localization accuracy. However,
obtaining reliable covariance values for map matching remains a complex task.
To address this challenge, we propose a neural network-based framework for
predicting localization error covariance in LiDAR map matching. To achieve
this, we introduce a novel dataset generation method specifically designed for
error covariance estimation. In our evaluation using a Kalman filter, we
achieved a 2 cm improvement in localization accuracy, a significant enhancement
in this domain.
