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Computer Science > Robotics

Title: Attention and Anticipation in Fast Visual-Inertial Navigation

Abstract: We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to estimate its state using an onboard 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 visual-inertial navigation? 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 leads to dramatic improvements in the VIN performance. In the easy scenarios, our approach outperforms the state-of-the-art in terms of localization errors. In the most challenging scenarios, it enables accurate visual-inertial navigation while the state of the art fails to track robot's motion during aggressive maneuvers.
Comments: 17 pages, 5 figures, 2 tables
Subjects: Robotics (cs.RO)
MSC classes: 68W01, 68W40, 68W25, 49K30
ACM classes: I.2.9; G.1.6
Cite as: arXiv:1610.03344 [cs.RO]
  (or arXiv:1610.03344v2 [cs.RO] for this version)

Submission history

From: Luca Carlone [view email]
[v1] Tue, 11 Oct 2016 14:05:31 GMT (5345kb,D)
[v2] Thu, 26 Oct 2017 00:41:45 GMT (5532kb,D)