Attention and Anticipation in Fast Visual-Inertial Navigation (1610.03344v3)

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.

• The paper demonstrates that task-driven selection of visual cues and forward-simulation anticipation significantly improve VIN performance in resource-constrained systems.
• The authors employ a greedy algorithm with submodularity guarantees to efficiently select task-relevant features over traditional appearance-based methods.
• Simulations and drone experiments confirm robust localization capabilities, paving the way for advanced autonomous navigation in challenging environments.

Attention and Anticipation in Fast Visual-Inertial Navigation

In the paper titled "Attention and Anticipation in Fast Visual-Inertial Navigation" by Luca Carlone and Sertac Karaman, the authors investigate a challenging problem in the domain of robotics and navigation: the efficient resource allocation for Visual-Inertial Navigation (VIN) under stringent computational constraints. This work is particularly relevant for scenarios where a robot, equipped with a camera and inertial sensors, must navigate and estimate its state without prior information about the external environment.

Core Contributions

The paper presents a task-driven framework for selecting visual cues that enhance the performance of VIN systems. This framework integrates four pivotal ideas:

1. Task-Driven Selection: The visual cues are chosen based on their relevance to improve the VIN performance metric, thus ensuring a focus on task-specific requirements rather than general visual feature quality.
2. Anticipation: The approach leverages forward-simulation models that predict the utility of visual cues over future time horizons, enabling anticipation of the robot's dynamics.
3. Efficiency and Simplicity: The selection algorithm is a greedy process, favoring simplicity and ease of implementation, which is crucial in real-time applications.
4. Performance Guarantees: The authors utilize properties of submodularity to provide formal guarantees that the greedy algorithm's performance is close to optimal.

Numerical Results and Claims

Simulations and real experiments on drones demonstrate the approach's efficacy in delivering state-of-the-art VIN performance while minimizing processing times. The authors claim that their method outperforms appearance-based feature selection techniques, offering more robust localization capabilities, especially in challenging scenarios involving aggressive maneuvers.

Implications and Future Work

The implications of this research are significant, particularly for applications in robotics where computational resources are limited, such as autonomous drones and mobile robots operating in GPS-denied environments. It paves the way for a more sophisticated understanding of task-driven perception, emphasizing the importance of prioritizing sensory inputs based on task relevance rather than raw data quality.

Looking forward, the authors suggest potential avenues for improvement, including parallelization of the greedy algorithm and exploration of learning-based enhancements to adapt to dynamically changing environments. This could open up broader applications in robotics and AI, where dynamic sensory input processing is crucial.

Conclusion

Carlone and Karaman's research offers a compelling approach to enhancing VIN systems via task-driven visual attention mechanisms. It aligns technical advancements with practical constraints, ensuring efficient navigation decisions in real-time with tight resource budgets. This methodology not only improves VIN performance but also sets a precedent for future developments in resource-constrained robotic autonomy.