Attention and Anticipation in Fast Visual-Inertial Navigation
Name
1610.03344.pdf
Description
Submitted version
Size
2.79 MB
Format
Adobe PDF
Checksum (MD5)
0bca3760a714bcf6f67b4c1a6ddba6b5
Author(s)
Carlone, Luca
Karaman, Sertac
Date Issued
July 2017
Journal
2017 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Carlone, Luca and Sertac Karaman. “Attention and Anticipation in Fast Visual-Inertial Navigation.” Paper presented at the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 29 May-3 June 2017, IEEE © 2017 The Author(s)
Version
Original manuscript
Abstract
We study a visual-inertial navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of VIN? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-The-Art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate VIN while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/TRO.2018.2872402
