Abstract:

In view of the problems of the existing laser SLAM algorithm in dynamic scenes， which has poor robustness and the positioning and mapping accuracy is easily disturbed by dynamic objects， a real-time dynamic laser SLAM algorithm called Object-SuMa that combines object-level geometric feature and semantic information is proposed. Firstly，through processes such as ground filtering， object segmentation and pose size calculation， object-level geometric features are generated and represented as texture and used to correct semantic segmentation errors within the object. Then， in the odometry stage， the IOU calculation of the oriented bounding box is decomposed， and object-level geometric weighting and semantic weighting are introduced based on the bounding box IOU and semantic segmentation results to reduce mismatching and dynamic point matching. In addition， the graphics rendering pipeline is used to build a parallel computing process， and the computational complexity and time consuming are reduced by two-step optimization of ground point registration and non-ground point registration. Finally， tests on the KITTI odometry data set show that compared with SuMa++， the Object-SuMa algorithm has improved the relative pose accuracy by 15% and reduced the average time of ICP by 17%， which improves the positioning accuracy and robustness of laser SLAM in dynamic scenarios.

Key words: laser SLAM, dynamic environment, object-level geometric feature, semantic information, parallel computing