Paolo Stegagno's website
Mutual localization with anonymous measurements
In a few recent works, we have formulated and investigated a novel problem called Mutual Localization with Anonymous Position Measurements (MLAPM). This is an extension of Mutual Localization with Position Measures, with the additional assumption that the identities of the measured robots are not known. MLAPM is obviously of interest in applications when individual tagging (e.g., by appearance or color) of the robots is impractical, expensive or undesirable. An interesting consequence of the anonymity hypothesis is that, for certain configurations of the multi-robot system, it causes a combinatorial ambiguity in the inversion of the measurement equation, resulting in the existence of multiple solutions. Similar conclusions can be drawn for the problem of Mutual Localization with Anonymous Bearing Measurements (MLABM), both in 2D and 3D.
A comprehensive approach on mutual localization with anonymous measurements can be found in [1] and [2].
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Initially [3,4] we have developed a two-phase filter for solving the MLAPM problem. The first phase uses MultiReg, an innovative algorithm aimed at obtaining sets of geometrically feasible relative pose hypotheses. In the second phase, the output of MultiReg is processed by a data associator and a multiple EKF to rate and select the best hypothesis. We have studied the performance of the developed localization system using both simulations and real experiments.
nderwater vehicles to wheeled robots on 3D terrain.
References
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Journal papers
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[1] A. Franchi, G. Oriolo and P. Stegagno, Mutual Localization in Multi-Robot Systems using Anonymous Relative Measurements, International Journal of Robotics Research, vol. 32, issue 11, pp. 1303-1322, Sept 2013. (download)
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[2] P. Stegagno, M. Cognetti, G. Oriolo, H. H. Bülthoff and A. Franchi, Ground and Aerial Mutual Localization using Anonymous Relative-Bearing Measurements, IEEE Transactions on Robotics, Oct 2016. (download)
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Conference papers
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[3] A. Franchi, G. Oriolo, and P. Stegagno, Mutual localization of a multi-robot team with anonymous relative position measures, Department of Computer and System Sciences, Tech. Rep. 1, Jan. 2009. (download)​​
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[4] A. Franchi, G. Oriolo, and P. Stegagno, Mutual localization in a multi-robot system with anonymous relative position measures. In 2009 IEEE/RSJ Int. Conf. on Intelligent Robot & Systems, St. Louis, MO, USA, pp. 3974-3980, Oct. 2009. (download)​​
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[5] A. Franchi, G. Oriolo, and P. Stegagno, On the solvability of the mutual localization problem with anonymous position measures. In 2010 IEEE Int. Conf. on Robotics and Automation, Anchorage, AK, USA, pp. 3193-3199, May 2010. (download)​​
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[6] A. Franchi, G. Oriolo, and P. Stegagno, Probabilistic mutual localization in multi-agent systems from anonymous position measures. In 49th IEEE Conference on Decision and Control, Atlanta, GA, USA, pp. 6534-6540, Dec. 2010. (download)​​
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[7] P. Stegagno, M. Cognetti, A. Franchi, and G. Oriolo, Mutual localization using anonymous bearing measurements. In 2011 IEEE/RSJ Int. Conf. on Intelligent Robot & Systems, San Francisco, CA, USA, pp. 469-474, Oct. 2011. (download)​​
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[8] M. Cognetti, P. Stegagno, A. Franchi, G. Oriolo, and H. H. Bülthoff, 3D Mutual localization with anonymous bearing measurements. In 2012 IEEE Int. Conf. on Robotics and Automation, St. Paul, MN, USA, May 2012. (download)​​
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In [5], we have investigated more in the detail the structure of the problem. We have found a necessary and sufficient condition for the uniqueness of the geometrical solution based on the notion of rotational symmetry in the physical plane. We have also derived the relationship between the number of robots and the number of possible solutions, and we have classified the solutions in a number of equivalence classes which is linear with the number of robots.
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In the same paper, we have developed a control law that effectively breaks symmetric formations so as to guarantee the unique solvability of the problem. We have demonstrated the performance of this control law through simulations.
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In [6] we have modified our localization system in a probabilistic sense, first by using particle filters (rather than EKFs) to compute the current belief on the robots' relative poses, and then by modifying MultiReg so that is can use this information as a feedback. This has several advantages, mainly (i) particle filters are intrinsically multi-modal and therefore do not require the use of heuristics for data association (ii) the new framework allows MultiReg to focus on solutions that are most likely according to the current belief, filtering out the effects of rotational symmetries that may arise in the system and avoiding the associated complexity increase. In practice, this results in a drastic reduction of the execution time whenever the task requires a rotational symmetric robot deployment (e.g., encirclement, escorting, etc). The proposed method is experimentally validated.
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For an application of MLAPM in a multi-robot task execution, see also the encirclement page.
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In [7] we have modified the system to solve the RML problem using anonymous bearing measurements, rather than full position (bearing plus distance) measurements. The new algorithm is then able to use measurements taken from non-depth sensors, such as video cameras, allowing the application of our method for mutual localization to a wider class of multi-agent systems.
In [8] we have extended the method proposed in [5] so as to solve the mutual localization problem in 3D environments using bearing-only measurements. Such extension is non-trivial, requiring not only a new multiple registration algorithm and a different filter, but also modifications to the system architecture and the use of a complementary filter to estimate the attitude based also on IMU data. The resulting localization system has been tested on a team of autonomous quadrotors, but in principle its field of application ranges from underwater vehicles to wheeled robots on 3D terrain.