Learning Scene Adaptive Covariance Error Model of LiDAR Scan Matching for Fusion Based Localization

Robust localization is an essential capability for autonomous land vehicles. While a lot of work focused on structured environments, this article focuses on navigation in off-road environments. In the off-road environment, due to the lack of salient features, scan matching algorithms tend to degenerate. Therefore, the first contribution of this paper is to propose a reliable degeneracy indicator which can evaluate the scan matching performance. The evaluated degeneracy indicator is then integrated into the factor graph optimization framework which is used in both the offline mapping system and the online localization system. Moreover, a complete navigation system that can handle the incomplete and partly outdated LiDAR maps is developed. Extensive tests on real-world data sets show that the proposed system outperforms state-of-the-art approaches, especially in degenerate scenarios.

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“…However, the accuracy of the prediction is closely related to the quality of the input descriptors. On the other hand, Ju et al (2019)…”

Section: Related Workmentioning

confidence: 99%

LiDAR‐based robust localization for field autonomous vehicles in off‐road environments

Ren

Xue

et al. 2021

Journal of Field Robotics

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FSM: Correspondenceless scan-matching of panoramic 2D range scans

Filotheou

Dimitriou

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Towards a Fully Automated 3D Reconstruction System Based on LiDAR and GNSS in Challenging Scenarios

Ren

Xue

et al. 2021

Remote Sensing

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High-precision 3D maps play an important role in autonomous driving. The current mapping system performs well in most circumstances. However, it still encounters difficulties in the case of the Global Navigation Satellite System (GNSS) signal blockage, when surrounded by too many moving objects, or when mapping a featureless environment. In these challenging scenarios, either the global navigation approach or the local navigation approach will degenerate. With the aim of developing a degeneracy-aware robust mapping system, this paper analyzes the possible degeneration states for different navigation sources and proposes a new degeneration indicator for the point cloud registration algorithm. The proposed degeneracy indicator could then be seamlessly integrated into the factor graph-based mapping framework. Extensive experiments on real-world datasets demonstrate that the proposed 3D reconstruction system based on GNSS and Light Detection and Ranging (LiDAR) sensors can map challenging scenarios with high precision.

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Learning Scene Adaptive Covariance Error Model of LiDAR Scan Matching for Fusion Based Localization

Cited by 3 publications

References 12 publications

LiDAR‐based robust localization for field autonomous vehicles in off‐road environments

LiDAR‐based robust localization for field autonomous vehicles in off‐road environments

FSM: Correspondenceless scan-matching of panoramic 2D range scans

Towards a Fully Automated 3D Reconstruction System Based on LiDAR and GNSS in Challenging Scenarios

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