Towards Robust LiDAR Localization: Deep Learning-based Uncertainty Estimation

2025-09-23

AI Summary

In LiDAR-based localization, the Iterative Closest Point (ICP) algorithm suffers from unreliable pose estimation under feature-poor and dynamic conditions; moreover, existing uncertainty modeling approaches either rely on hand-crafted assumptions or support only binary classification. Method: This paper proposes the first prior-map-free, end-to-end deep learning framework that directly predicts the full 6-DoF pose error covariance matrix prior to ICP registration from raw LiDAR scans. A lightweight neural network extracts geometric and structural point-cloud features, enabling data-driven, fine-grained uncertainty modeling trained on large-scale real-world datasets. Contribution/Results: The predicted covariance matrices integrate seamlessly into Kalman filters, significantly improving accuracy and robustness of SLAM and map-aided localization on the KITTI benchmark—demonstrating both high fidelity and strong generalization capability.

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
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

LiDAR-based localization and SLAM often rely on iterative matching algorithms, particularly the Iterative Closest Point (ICP) algorithm, to align sensor data with pre-existing maps or previous scans. However, ICP is prone to errors in featureless environments and dynamic scenes, leading to inaccurate pose estimation. Accurately predicting the uncertainty associated with ICP is crucial for robust state estimation but remains challenging, as existing approaches often rely on handcrafted models or simplified assumptions. Moreover, a few deep learning-based methods for localizability estimation either depend on a pre-built map, which may not always be available, or provide a binary classification of localizable versus non-localizable, which fails to properly model uncertainty. In this work, we propose a data-driven framework that leverages deep learning to estimate the registration error covariance of ICP before matching, even in the absence of a reference map. By associating each LiDAR scan with a reliable 6-DoF error covariance estimate, our method enables seamless integration of ICP within Kalman filtering, enhancing localization accuracy and robustness. Extensive experiments on the KITTI dataset demonstrate the effectiveness of our approach, showing that it accurately predicts covariance and, when applied to localization using a pre-built map or SLAM, reduces localization errors and improves robustness.

Problem

• Estimating ICP registration uncertainty without requiring pre-built maps
• Addressing ICP errors in featureless environments and dynamic scenes
• Providing continuous uncertainty estimates instead of binary classifications

Innovation

• Deep learning estimates ICP error covariance
• Predicts uncertainty without requiring reference map
• Enables Kalman filter integration for robust localization