Reliable-loc: Robust sequential LiDAR global localization in large-scale street scenes based on verifiable cues

2024-11-09

arXiv.org

AI Summary

To address the insufficient robustness of LiDAR global localization in large-scale outdoor environments—particularly under sparse feature conditions and incomplete prior map coverage—this paper proposes a novel Monte Carlo localization framework tailored for wearable laser scanning (WLS). Methodologically, it introduces (1) a weighted particle filter that jointly leverages spatially verifiable local features and temporally verifiable sequential cues, and (2) a state monitoring mechanism that models pose uncertainty over time and enables dynamic mode switching, facilitating heterogeneous point cloud integration between WLS and mobile laser scanning (MLS). Evaluated on a multi-scenario, heterogeneous dataset spanning over 30 km, the system achieves 2.91 m positional accuracy and 3.74° heading accuracy while meeting real-time constraints. These results demonstrate significantly improved long-term localization reliability in complex, unstructured outdoor environments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Wearable laser scanning (WLS) system has the advantages of flexibility and portability. It can be used for determining the user's path within a prior map, which is a huge demand for applications in pedestrian navigation, collaborative mapping, augmented reality, and emergency rescue. However, existing LiDAR-based global localization methods suffer from insufficient robustness, especially in complex large-scale outdoor scenes with insufficient features and incomplete coverage of the prior map. To address such challenges, we propose LiDAR-based reliable global localization (Reliable-loc) exploiting the verifiable cues in the sequential LiDAR data. First, we propose a Monte Carlo Localization (MCL) based on spatially verifiable cues, utilizing the rich information embedded in local features to adjust the particles' weights hence avoiding the particles converging to erroneous regions. Second, we propose a localization status monitoring mechanism guided by the sequential pose uncertainties and adaptively switching the localization mode using the temporal verifiable cues to avoid the crash of the localization system. To validate the proposed Reliable-loc, comprehensive experiments have been conducted on a large-scale heterogeneous point cloud dataset consisting of high-precision vehicle-mounted mobile laser scanning (MLS) point clouds and helmet-mounted WLS point clouds, which cover various street scenes with a length of over 30 km. The experimental results indicate that Reliable-loc exhibits high robustness, accuracy, and efficiency in large-scale, complex street scenes, with a position accuracy of 2.91 m, yaw accuracy of 3.74 degrees, and achieves real-time performance. For the code and detailed experimental results, please refer to https://github.com/zouxianghong/Reliable-loc.

Problem

• Enhances LiDAR global localization robustness in large-scale scenes
• Addresses insufficient features and incomplete prior map coverage issues
• Improves accuracy and real-time performance in complex street environments

Innovation

• MCL based on spatially verifiable cues
• Localization status monitoring mechanism
• Adaptive mode switching with temporal cues