Multi-robot in-ice localization using graph optimization

by Joachim Clemens

Abstract:

We present a graph-based algorithm for jointly estimating the positions of multiple ice-melting probes. The probes determine the distances relative to each other by measuring the signal propagation time of acoustic pulses. Furthermore, multiple other sensors, like an inertial measurement unit and a differential magnetometer system, are used to calculate the relative movement of the probes. The positions of the probes are represented by nodes of a graph, while those nodes are constrained by edges, which result from the sensor measurements. Finally, the localization task is solved by optimizing the node positions with respect to the error resulting from the constraints. Our approach is compared to other algorithms for multi-robot localization in different scenarios.

Reference:

Multi-robot in-ice localization using graph optimization (Joachim Clemens), In IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), IEEE, 2017.

Bibtex Entry:

@INPROCEEDINGS{clemens2017multi, author = {Clemens, Joachim}, month = apr, title = {Multi-robot in-ice localization using graph optimization}, booktitle = {IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)}, year = {2017}, pages = {36-42}, publisher = {IEEE}, location = {Coimbra, Portugal}, organization = {IEEE}, doi = {10.1109/ICARSC.2017.7964049}, url = {http://ieeexplore.ieee.org/document/7964049/}, abstract = {We present a graph-based algorithm for jointly estimating the positions of multiple ice-melting probes. The probes determine the distances relative to each other by measuring the signal propagation time of acoustic pulses. Furthermore, multiple other sensors, like an inertial measurement unit and a differential magnetometer system, are used to calculate the relative movement of the probes. The positions of the probes are represented by nodes of a graph, while those nodes are constrained by edges, which result from the sensor measurements. Finally, the localization task is solved by optimizing the node positions with respect to the error resulting from the constraints. Our approach is compared to other algorithms for multi-robot localization in different scenarios.}, }