Neural Error Covariance Estimation for Precise LiDAR Localization

2025-01-05

AI Summary

In LiDAR-based map matching, accurate modeling of localization error covariance remains challenging due to data degeneracy, limiting robustness in safety-critical autonomous driving. Method: This paper introduces the first end-to-end neural framework explicitly designed for error covariance estimation. We propose a novel hybrid data generation strategy combining synthetic and real-world scenes, enabling direct learning of LiDAR localization uncertainty—rather than pose alone—and embed the neural-predicted covariance into a Kalman filter for uncertainty-aware tightly coupled fusion. Contribution/Results: Evaluated on real-world benchmarks, our approach improves absolute positioning accuracy by 2 cm, substantially surpassing existing LiDAR uncertainty modeling methods. It breaks the long-standing bottleneck in probabilistic LiDAR localization and establishes a new paradigm for high-reliability, uncertainty-aware autonomous vehicle positioning.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Large-scale security measurements

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.

Problem

• LiDAR
• Autonomous Vehicles
• Positioning Accuracy

Innovation

• Neural Networks
• Data Generation Method
• Kalman Filter Accuracy Enhancement