Information of Opportunity-Aided VI-SLAM for Enhancing Accuracy in Perpetual GNSS-Denied Environments Without Loop Closure

Classical visual–inertial simultaneous localization and mapping (VI-SLAM) systems provide accurate relative motion estimation but are plagued by accumulated drift in long-term global navigation satellite system (GNSS)-denied environments where loop closures are unavailable. To address this fundamental challenge, we propose a novel framework that leverages “Information of Opportunity” (IOP)—random and heterogeneous diverse external signals including terrestrial radio signals, low Earth orbit (LEO) satellite observations or map data to achieve drift-less geo-localization. Our system integrates three core components: an enhanced visual–inertial front-end with information-theoretic feature selection (FS) that adaptively selects informative visual features for robust and efficient tracking; a versatile IOP processing pipeline that acquires and aligns absolute positioning cues (e.g., from LEO pseudoranges or image-to-map matching) into a unified global reference frame for enforcing global drift-free constraints; and a tightly coupled optimization back-end that jointly infers IOP-derived global constraints with local visual–inertial measurements within a unified factor graph, enabling continuous drift-less localization. Extensive evaluations on the karlsruhe institute of technology and toyota technological institute at chicago dataset (KITTI) odometry dataset, GNSS visual-inertial navigation system (GVINS) dataset and real-world urban driving experiments, conducted with stereo-infrared cameras, inertial measurement unit (IMU), and Iridium LEO receiver, demonstrate that our method effectively suppresses cumulative drift and achieves globally drift-less localization.