Optimization and Implementation of SLAM Algorithm for Intelligent Vehicles under LIO-SAM Framework
To address the issues of redundant keyframe extraction during local map construction and invalid loop closures in loop detection for laser-based SLAM,this study adopts a method based on the LIO-SAM framework.It utilizes a vector container,Kd-tree nearest neighbor search,and VoxelGrid filter to avoid redundant extraction of keyframes near the current frame.For loop detection,algorithms based on scan context and distance-based methods are introduced.By setting a threshold for frame sequence differences,loop frames are screened to reduce the number of loop detections in scenarios such as waiting at traffic lights or yielding to pedestrians.Experimental results show that,compared to LIO-SAM,the proposed algorithm reduces the average keyframe extraction time near the current frame by 31.39%and the number of loop detections by 32.5%,without compromising mapping accuracy.This significantly enhances computational efficiency and robustness,providing an effective method for optimizing resource utilization and algorithm performance.
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