Attention and Anticipation in Fast Visual-Inertial Navigation

TL;DR

This paper introduces a resource-efficient, task-driven visual cue selection method for fast visual-inertial navigation that anticipates future utility, ensuring high performance in constrained computational environments.

Contribution

It proposes a novel, anticipatory, greedy algorithm for visual cue selection in VIN that guarantees near-optimal performance with formal submodularity-based bounds.

Findings

Outperforms appearance-based feature selection in simple scenarios.

Enables accurate navigation during aggressive maneuvers in challenging environments.

Maintains state-of-the-art performance with reduced computational load.

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 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 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 visual-inertial navigation while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.