A Back-End Optimization Algorithm of SLAM Based onGraph Model with Prior Points

Objective Simultaneous localization and mapping(SLAM)is a hot issue in the field of robotics,theproblem consists of two parts,front-end perception and back-end optimization.At present,the back-end optimization algorithm based on factor graph works well.In the factor graph,nodes represent po-ses,the edges between nodes represents the range information and closed loop constraint.Since theaccuracy differences of nodes are not described in factor graph,the global positioning accuracy can beimproved no further.To solve this problem,we propose a back-end optimization algorithm based ongraph model with prior points which are introduced from the front-end that updates the graph modelby fixing high-precision poses during optimization.The back-end can thereby use these fixed high-pre-cision poses to drag low-precision poses closer to the ground truth and increase overall accuracy.Wedemonstrate the approach and present results on public datasets.The experimental results show thatthe maps acquired with our method show increased global precision.