UNSEEN: Uncertainty-aware Navigation via Sparse Estimation in Unknown Environments

• URL: http://arxiv.org/abs/2606.20755v1
• Date: Thu, 18 Jun 2026 07:54:07 GMT
• Title: UNSEEN: Uncertainty-aware Navigation via Sparse Estimation in Unknown Environments
• Abstract summary: Vision-only navigation offers a lightweight alternative, but its performance degrades severely under motion blur, low texture, and illumination changes.<n>We present UNSEEN, a unified uncertainty- and perception-aware navigation framework that explicitly couples localization, mapping, and planning using only a front-mounted camera.
• Score: 5.893948403053389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual navigation in unknown environments remains a core challenge in mobile robotics, especially for resource-constrained platforms. Most existing approaches rely on loosely coupled modular pipelines and strong assumptions on perception quality or environmental structure, often resorting to multi-modal sensor suites that increase system complexity and deployment cost. Vision-only navigation offers a lightweight alternative, but its performance degrades severely under motion blur, low texture, and illumination changes, largely because they neglect the tight coupling between commanded motion and perception. While perception-aware methods partially address this issue, they typically optimize individual modules and fail to propagate uncertainty consistently across the navigation stack. In this paper, we present UNSEEN, a unified uncertainty- and perception-aware navigation framework that explicitly couples localization, mapping, and planning using only a front-mounted camera. UNSEEN estimates sparse maps and robot poses with associated uncertainties at 6Hz, and leverages them to plan trajectories that jointly optimize task progress and estimation accuracy in receding-horizon. Simulations and extensive real-world experiments in unknown environments demonstrate the robustness of the proposed approach, with UNSEEN-SLAM reducing absolute translational error by 9.8% and UNSEEN-Plan improving estimation accuracy by up to 45% compared to state-of-the-art methods, while achieving a 100% task success rate.

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