collaborators

7 papers

cs.RO2026

SASGeo: Stability-Aware Semantic Map Localization for GNSS-Denied UAVs -- A Framework and Synthetic Proof of Concept

Natalia Trukhina, Vadim Vashkelis

GNSS-denied unmanned aerial vehicles require occasional absolute position fixes to bound the drift of visual-inertial odometry. Cross-view image retrieval can provide such fixes, b…

cs.LG2026

SemanticZip: A Pilot Framework for Lossy Text Compression with LLMs as Semantic Decompressors

Natalia Trukhina, Vadim Vashkelis

Text compression for large language model (LLM) systems is usually framed as token deletion, retrieval, summarization, or exact reconstruction. We study a more aggressive but expli…

cs.LG2026

Compress the Context, Keep the Commitments: A Formal Framework for Verifiable LLM Context Compression

Natalia Trukhina, Vadim Vashkelis

LLM context is not just tokens; it is a set of commitments. Long-running conversations accumulate goals, constraints, decisions, preferences, tool results, retrieved evidence, arti…

cs.CV2026

Mobile Traffic Camera Calibration from Road Geometry for UAV-Based Traffic Surveillance

Alexey Popov, Natalia Trukhina, Vadim Vashkelis

Unmanned aerial vehicles (UAVs) can provide flexible traffic surveillance where fixed roadside cameras are unavailable, costly, or impractical. However, raw UAV video is difficult…

cs.CV2026

Hybrid Visual Telemetry for Bandwidth-Constrained Robotic Vision: A Pilot Study with HEVC Base Video and JPEG ROI Stills

Natalia Trukhina, Vadim Vashkelis

Bandwidth-constrained robotic and surveillance systems often rely on a single compressed video stream to support both continuous scene awareness and downstream machine perception.…

cs.SE2026

RISC-V Functional Safety for Autonomous Automotive Systems: An Analytical Framework and Research Roadmap for ML-Assisted Certification

Nick Andreasyan, Mikhail Struve, Alexey Popov +2

RISC-V is emerging as a viable platform for automotive-grade embedded computing, with recent ISO 26262 ASIL-D certifications demonstrating readiness for safety-critical deployment…