GM-Livox: An Integrated Framework for Large-Scale Map Construction with Multiple Non-repetitive Scanning LiDARs

TL;DR

This paper presents GM-Livox, a comprehensive framework that integrates multiple non-repetitive scanning LiDARs with inertial sensors, GNSS, and encoders to improve large-scale map construction and pose estimation for autonomous vehicles.

Contribution

The paper introduces a novel multi-LiDAR integration scheme with precise synchronization, feature merging, and a factor graph optimization, enhancing robustness and accuracy in large-scale mapping.

Findings

Multi-LiDAR integration improves robustness in challenging scenarios.

The framework achieves real-time performance with multi-threaded computation.

Experimental results demonstrate increased accuracy and system resilience.

Abstract

With the ability of providing direct and accurate enough range measurements, light detection and ranging (LiDAR) is playing an essential role in localization and detection for autonomous vehicles. Since single LiDAR suffers from hardware failure and performance degradation intermittently, we present a multi-LiDAR integration scheme in this article. Our framework tightly couples multiple non-repetitive scanning LiDARs with inertial, encoder, and global navigation satellite system (GNSS) into pose estimation and simultaneous global map generation. Primarily, we formulate a precise synchronization strategy to integrate isolated sensors, and the extracted feature points from separate LiDARs are merged into a single sweep. The fused scans are introduced to compute the scan-matching correspondences, which can be further refined by additional real-time kinematic (RTK) measurements. Based thereupon, we construct a factor graph along with the inertial preintegration result, estimated ground constraints, and RTK data. For the purpose of maintaining a restricted number of poses for estimation, we deploy a keyframe based sliding-window optimization strategy in our system. The real-time performance is guaranteed with multi-threaded computation, and extensive experiments are conducted in challenging scenarios. Experimental results show that the utilization of multiple LiDARs boosts the system performance in both robustness and accuracy.