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

TL;DR

This paper introduces Reliable-loc, a robust LiDAR-based global localization method that uses verifiable cues and adaptive mechanisms to achieve high accuracy and real-time performance in large-scale outdoor street scenes.

Contribution

It proposes a novel localization approach combining Monte Carlo Localization with verifiable cues and an adaptive status monitoring mechanism for improved robustness in complex environments.

Findings

Achieves 2.91 m position accuracy and 3.74° yaw accuracy.

Demonstrates high robustness and efficiency in large-scale street scenes.

Operates in real-time with comprehensive experimental validation.

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.