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- W4386875498 abstract "Achieving accurate, efficient, and consistent localization within an a priori environment map remains a fundamental challenge in robotics and computer vision. Conventional map-based keyframe localization often suffers from sub-optimal viewpoints due to limited field of view (FOV), thus degrading its performance. To address this issue, in this paper, we design a real-time tightly-coupled Neural Radiance Fields (NeRF)-aided visual-inertial navigation system (VINS), termed NeRF-VINS. By effectively leveraging NeRF's potential to synthesize novel views, essential for addressing limited viewpoints, the proposed NeRF-VINS optimally fuses IMU and monocular image measurements along with synthetically rendered images within an efficient filter-based framework. This tightly coupled integration enables 3D motion tracking with bounded error. We extensively compare the proposed NeRF-VINS against the state-of-the-art methods that use prior map information, which is shown to achieve superior performance. We also demonstrate the proposed method is able to perform real-time estimation at 15 Hz, on a resource-constrained Jetson AGX Orin embedded platform with impressive accuracy." @default.
- W4386875498 created "2023-09-20" @default.
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- W4386875498 date "2023-09-17" @default.
- W4386875498 modified "2023-10-18" @default.
- W4386875498 title "NeRF-VINS: A Real-time Neural Radiance Field Map-based Visual-Inertial Navigation System" @default.
- W4386875498 doi "https://doi.org/10.48550/arxiv.2309.09295" @default.
- W4386875498 hasPublicationYear "2023" @default.
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