Indoor Magnetic Pose Graph SLAM with Robust Back-end

Abstract

In this paper, a method of solving a simultaneous localizationand mapping (SLAM) problem is proposed by employing pose graph optimization and indoor magnetic eld measurements. The objective of pose graph optimization is to estimate the robot trajectory from the constraints of relative pose measurements. Since the magnetic eld in indoor environments is stable in a temporal domain and suciently varying in a spatial domain, these characteristics can be exploited to generate the constraints in pose graphs. In this paper two types of constraints are designed, one is for local heading correction and the other for loop closing. For the loop closing constraint, sequence-based matching is employed rather than a singlemeasurement-based one to mitigate the ambiguity of magnetic measurements. To improve the loop closure detection we further employed existing robust back-end methods proposed by other researchers. Experimental results show that the proposedSLAM system with only wheel encoders and a single magnetometer o ers comparable results with a reference-level SLAM system in terms of robot trajectory, thereby validating the feasibility of applying magnetic constraints to the indoor posegraph SLAM.