Graph-based SLAM Using Architectural Floor Plans Without Loop Closure.

In simultaneous localization and mapping (SLAM), map distortions appear due to the accumulation of pose estimation errors. Loop closure of graph-based SLAM is known as one of the major solutions to this problem. However, there was still a problem that the errors remain in typical loop closure because it only uses constraints of the relative poses of nodes for error correction. In response, the authors focused on the fact that the environments in architectural floor plans are described accurately. Therefore we propose a method using constraints of the absolute poses based on floor plans. Specifically, the robot pose estimated on the floor plan is used as the absolute pose constraint. By optimizing the pose graph that includes absolute pose constraints, a map can be built more accurately to align with the floor plan. Although SLAM using floor plan has been studied before, we propose a new method to reduce computational cost while maintaining map accuracy without loop closure. In this study, we evaluated the effectiveness of our method by comparison with two other methods, one using loop closure and the other using both loop closure and absolute pose constraints. The results proved that the proposed method can maintain the map accuracy and reduce the computational cost.