Abstract

In this paper, a method of solving a simultaneous localization and mapping (SLAM) problem is proposed by employing pose graph optimization and indoor magnetic field measurements. The objective of pose graph optimization is to estimate the robot trajectory from the constraints of relative pose measurements. Since the magnetic field in indoor environments is stable in a temporal domain and sufficiently 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 single measurement-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 proposed SLAM system with only wheel encoders and a single magnetometer offers comparable results with a reference-level SLAM system in terms of robot trajectory, thereby validating the feasibility of applying magnetic constraints to the indoor pose graph SLAM.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.