基于多源信息融合的井下无人驾驶建图与定位方法

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中图分类号：TD525 文献标志码：A

Abstract：Due to the harsh environment in coal mines underground，mapping methods based on singlesource odometry information are prone to issues such as drift，occlusion，and missing semantic features.Existing mainstream localization algorithms applied underground in coal mines often encounter localization errors.To addressthese issues，this paper proposed an underground autonomous mapping and localization method basedon multi-source information fusion.The mapping was performed using themulti-source information fusion-based RTAB-Map algorithm，which significantly reduced mapping driftand improved featurecapture ability by fusing point cloud and image data.Precise localization was achieved using the Adaptive Monte Carlo Localization （AMCL）algorithm，which combined LiDAR and motion information and employed particle filtering，pose prediction and resampling to achieve adaptive localization，thereby reducing localization inaccuracies and mapping drift.Simulationand experimental resultsshowed that，compared withasingle wheel odometry，the absolute value of the relative error of RTAB-Map mapping based on multi-source information fusion was reduced to within 1% ，and the map matching accuracy was higher，improving mapping reliability.Particles using the AMCLalgorithm converged rapidly within 2 meters，meeting the localization requirements of autonomous auxiliary transport vehicles.

Key Words： underground autonomous driving；SLAM;multi-source information fusion；RTAB-Map algorithm;AMCL algorithm

0引言

无人驾驶辅助运输车辆为煤矿智能化的关键部分。（剩余11652字）