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Neural Error Covariance Estimation for Precise LiDAR Localization

Minoo Dolatabadi, Fardin Ayar, Ehsan Javanmardi, Manabu Tsukada, Mahdi Javanmardi

TL;DR

This paper tackles the problem of precise LiDAR-based localization by predicting the localization error covariance for map matching using a neural network. It introduces a Monte Carlo dataset generation method to produce ground-truth $6\times6$ covariances and enforces positive definiteness through a Cholesky-based parameterization, enabling end-to-end training. Evaluated on KITTI Odometry, the approach achieves a notable 2 cm improvement when the predicted covariances are integrated into a Kalman filter with an IMU motion model and ICP corrections. The findings highlight the practical potential of neural covariance prediction to enhance the robustness and accuracy of autonomous-vehicle localization and suggest extensions to multi-sensor fusion.

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.

Neural Error Covariance Estimation for Precise LiDAR Localization

TL;DR

This paper tackles the problem of precise LiDAR-based localization by predicting the localization error covariance for map matching using a neural network. It introduces a Monte Carlo dataset generation method to produce ground-truth covariances and enforces positive definiteness through a Cholesky-based parameterization, enabling end-to-end training. Evaluated on KITTI Odometry, the approach achieves a notable 2 cm improvement when the predicted covariances are integrated into a Kalman filter with an IMU motion model and ICP corrections. The findings highlight the practical potential of neural covariance prediction to enhance the robustness and accuracy of autonomous-vehicle localization and suggest extensions to multi-sensor fusion.

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.
Paper Structure (16 sections, 8 equations, 2 figures, 3 tables, 1 algorithm)

This paper contains 16 sections, 8 equations, 2 figures, 3 tables, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORKS
  3. Predicting Localization Error
  4. Covariance Prediction
  5. Proposed Approach
  6. Pose Representation
  7. Overview
  8. Dataset Generation Method
  9. Training
  10. Loss Function
  11. EXPERIMENT AND EVALUATION
  12. Dataset Generation
  13. Evaluation Metrics
  14. Results
  15. Evaluation of Sensor Fusion
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Overview of the proposed approach. In the upper section, a map is built from the input point cloud, followed by ICP matching to generate covariance for each point cloud. In the lower section, the input point cloud from the LiDAR sensor is processed through a neural network. The entire network, including feature extraction and regression, is trained end-to-end.
  • Figure 2: Visualization of computed covariance on the corresponding point cloud in sequence 00.