Research On SLAM Algorithm Based On Fusion Of Lidar And IMU

The field of robotics has been booming in recent years,and high-precision mapping is one of the necessary conditions to support autonomous navigation of robots.Through SLAM technology,high-precision maps can be quickly built.SLAM technology enables a mobile robot,equipped with specific sensors and related algorithms,to autonomously construct an environmental model and calculate its own position without any prior environmental information during its movement.This greatly enhances the autonomous navigation capabilities of mobile robots.Based on this,this article integrates two sensors,Lidar and IMU,to conduct research on SLAM technology in order to obtain more accurate high-precision maps.The main content of this article includes:(1)Using the LIO-SAM algorithm to build high-precision maps may have a problem of insufficient robot positioning accuracy,which prevents the construction of accurate high-precision maps.Based on this,this article proposes the TDE-LIO-SAM algorithm,which applies the time-distance-entropy reduction strategy in the loop-closure detection module of the LIO-SAM algorithm,which can effectively improve the robot’s positioning accuracy and obtain more accurate high-precision maps.This algorithm first uses time and distance thresholds to filter candidate loop closures,and then uses the entropy reduction strategy to further filter the candidate loop closures to obtain the correct loop closure matching,and then corrects the robot’s pose.Finally,the corrected pose is used as the global pose to build the map.(2)Using the TDE-LIO-SAM algorithm can effectively improve the accuracy of robot localization and obtain precise high-accuracy maps,but the algorithm’s running speed is not fast.Based on this,this article uses an efficient data structure called ikd-Tree for dynamic space partitioning.Ikd-Tree uses an incremental method to update the tree,so it is much shorter than existing kd-Trees in terms of tree construction time.At the same time,ikd-Tree supports box operations and can quickly and batch update nodes.By applying ikd-Tree to the TDE-LIO-SAM algorithm,the algorithm’s map construction speed can be significantly improved.(3)Based on ikd-Tree,the TDE-LIO-SAM algorithm can quickly obtain precise highaccuracy maps,but there are still dynamic objects in the map,such as pedestrians and moving vehicles,which can affect the subsequent use of the map.Based on this,this article proposes a dynamic object removal method based on improved raster point cloud distribution differences,which can effectively remove dynamic objects from the map and obtain better high-accuracy maps.The algorithm first determines "interest areas" in the map using the VIO algorithm,then selects possible dynamic areas in the map using the R-POD and SRT algorithms,and finally removes dynamic points in the possible dynamic areas using the R-VPF and R-GPF algorithms,resulting in a more accurate high-accuracy map.