Key points are not available for this paper at this time.
ABSTRACT Unmanned mining technology is essential for enhancing safety, increasing efficiency, and reducing operational costs. The complex and hazardous nature of mining environments demands advanced positioning systems for autonomous vehicles, with laser simultaneous localization and mapping (SLAM) algorithms playing a critical role. This paper provides a systematic review of the core technical modules within laser SLAM algorithms, analyzing their development trends, strengths, and weaknesses. A comprehensive evaluation of fifteen mainstream SLAM algorithms on the AutoMine open‐pit mining data set reveals significant insights. Experimental results demonstrate that traditional feature‐based algorithms are prone to significant trajectory drift due to feature loss in sparse‐feature mining environments. Notably, the study further identifies a specific “Ramp Drift” mechanism where recursive estimators suffer Z ‐axis instability on monotonic slopes. Comparative analysis suggests that while Light Detection and Ranging (LiDAR)–inertial fusion generally enhances robustness, degeneracy‐aware architectures are the decisive factor for stability. Specifically, the LiDAR‐only odometry GLO achieves the highest stability in relative pose error due to its Weighted Elastic Matching strategy, while the Adaptive‐LIO demonstrates superior global consistency in absolute pose error. This study highlights a current lack of SLAM architectures specifically optimized for the unique challenges of open‐pit mines. Future research should focus on feature extraction enhancement in open scenes, the development of optimized LiDAR–inertial–RTK fusion architectures, and the integration of artificial intelligence to improve adaptability in dynamic and degraded scenarios.
Wu et al. (Mon,) studied this question.