Robust Pose-Graph SLAM Using Absolute Orientation Sensing

Saurav Agarwal; Karthikeya S. Parunandi; Suman Chakravorty

RA-L 20197 citations

Robust Pose-Graph SLAM Using Absolute Orientation Sensing

Saurav Agarwal, Karthikeya S. Parunandi, Suman Chakravorty

Abstract

It is known that in the simultaneous localization and mapping (SLAM) problem when a robot's orientation is known, an estimation of the history of its poses can be formulated as a standard linear least squares problem. In this letter, we exploit this property of SLAM to develop a robust pose-graph SLAM framework that uses absolute orientation sensing. Our contribution are as follows: 1) we show that absolute orientation can be estimated using local structural cues; and 2) we develop a method to incorporate absolute orientation measurements in both the front and back-end of pose-graph SLAM. We also demonstrate our approach through extensive simulations and a physical real-world demonstration along with comparisons against existing state-of-the-art solvers that do not use absolute orientation.

BibTeX

@inproceedings{ral2019_robustposegraphs,
  title = {Robust Pose-Graph SLAM Using Absolute Orientation Sensing},
  author = {Saurav Agarwal and Karthikeya S. Parunandi and Suman Chakravorty},
  booktitle = {RA-L 2019},
  year = {2019}
}