2025/10/01 by Reza Vafaee, Vafaee, Reza, Kian Behzad +7 · 1 voice · 1 citation
Computer Science · Engineering · #Advanced Neural Network Applications #FOS: Computer and information sciences #FOS: Electrical engineering #Gaze Tracking and Assistive Technology #Robotics (cs.RO) #Systems and Control (eess.SY) #Visual Attention and Saliency Detection #cs.RO #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2510.00942
openalex publication_date 2025/10/01 · arxiv published 2025/10/01 · arxiv updated 2025/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a task-oriented computational framework to enhance Visual-Inertial Navigation (VIN) in robots, addressing challenges such as limited time and energy resources. The framework strategically selects visual features using a Mean Squared Error (MSE)-based, non-submodular objective function and a simplified dynamic anticipation model. To address the NP-hardness of this problem, we introduce four polynomial-time approximation algorithms: a classic greedy method with constant-factor guarantees; a low-rank greedy variant that significantly reduces computational complexity; a randomized greedy sampler that balances efficiency and solution quality; and a linearization-based selector based on a first-order Taylor expansion for near-constant-time execution. We establish rigorous performance bounds by leveraging submodularity ratios, curvature, and element-wise curvature analyses. Extensive experiments on both standardized benchmarks and a custom control-aware platform validate our theoretical results, demonstrating that these methods achieve strong approximation guarantees while enabling real-time deployment.