In this paper, we address the problem of cooperative mapping (CM) using datasets collected by multiple users at different times, when the transformation between the users' starting poses is unknown. Specifically, we formulate CM as a constrained optimization problem, in which each user's independently estimated trajectory and map are merged together by imposing geometric constraints between commonly observed point and line features. Additionally, we provide an algorithm for efficiently solving the CM problem, by taking advantage of its structure. The proposed solution is proven to be batch-least-squares (BLS) optimal over all users' datasets, while it is less memory demanding and lends itself to parallel implementations. In particular, our solution is shown to be faster than the standard BLS solution, when the overlap between the users' data is small. Furthermore, our algorithm is resource-aware as it is able to consistently trade accuracy for lower processing cost, by retaining only an informative subset of the common-feature constraints. Experimental results based on visual and inertial measurements collected from multiple users within large buildings are used to assess the performance of the proposed CM algorithm.
Bibliographical noteFunding Information:
Manuscript received December 14, 2017; accepted April 28, 2018. Date of publication August 9, 2018; date of current version October 2, 2018. This paper was recommended for publication by Associate Editor V. Kyrki and Editor C. Torras upon evaluation of the reviewers’ comments. This work was supported in part by Google LLC, in part by Project Tango, and in part under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 (LLNL-JRNL-751808). (Corresponding author: Chao X. Guo.) C. X. Guo and E. D. Nerurkar are with Google Daydream, Mountain View, CA 94043 USA (e-mail:,firstname.lastname@example.org; email@example.com).
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- Cooperative mapping (CM)
- constrained optimization problem
- resource-aware system
- three-dimensional (3-D) mapping
- visual and inertial sensor fusion