{ "domain": "aervimo.com", "productFacts": { "brand": { "name": "aervimo engineering", "wordmark": "aervimo", "descriptor": "ENGINEERING", "email": "cto@aervimo.com", "domain": "https://aervimo.com", "eyebrow": "Air–ground mapping · Visual positioning", "headline": [ "See the coverage.", "Understand", "the position." ], "intro": "AG-RMS brings vehicle and drone mapping into one synchronized field view. ZhiTu VisNav explores map-referenced visual navigation, with a reported 740-frame campus trial and a clear embedded-engineering roadmap.", "promise": "A clearer picture of the ground ahead.", "short": "AG-RMS air–ground real-time mapping and ZhiTu VisNav visual navigation.", "nav": "Systems", "section": "Two capabilities. Two clear evaluation paths.", "approach": "From field questions to a defined evaluation.", "process": [ [ "Define the environment", "Identify the civilian application, capture area and operating conditions." ], [ "Review the inputs", "Check sensors, reference maps, data access and integration boundaries." ], [ "Run a scoped evaluation", "Measure coverage or positioning against agreed reference data." ], [ "Review before scaling", "Document failure cases, support needs and the next engineering milestone." ] ], "applications": [ [ "Urban mapping and real-scene 3D", "AG-RMS combines vehicle and aerial viewpoints to review occlusions and complete targeted capture while the team is still on site.", "air-ground-mapping" ], [ "Road and energy-corridor assets", "Use a shared capture view for road asset inventories, maintenance surveys and power/energy corridor mapping. The profile reports work rate >20 km/day for its described configuration.", "air-ground-mapping" ], [ "Mapping with limited connectivity", "The described AG-RMS field workflow runs on an encrypted in-vehicle LAN, supporting mapping and civilian emergency assessment without public internet access.", "air-ground-mapping" ], [ "Civilian visual-positioning research", "ZhiTu VisNav provides a prototype evaluation path informed by 740 campus localization frames, 9.8 m median deviation at 120 m altitude and current RK3588 engineering work.", "visual-positioning" ] ], "about": "aervimo engineering focuses on air–ground mapping coordination and map-referenced visual positioning for civilian applications. The mapping workflow is intended for scoped pilot discussions. Visual positioning remains a prototype-stage research program for engineering evaluation. These are separate capabilities, with distinct maturity and acceptance criteria.", "items": [ "air-ground-mapping", "visual-positioning" ], "highlights": [ { "slug": "air-ground-mapping", "index": 0 }, { "slug": "air-ground-mapping", "index": 1 }, { "slug": "visual-positioning", "index": 0 }, { "slug": "visual-positioning", "index": 1 } ] }, "products": [ { "slug": "air-ground-mapping", "brand": "aervimo", "title": "AG-RMS air–ground real-time mapping system", "label": "AG-RMS mapping system", "category": "MAPPING COORDINATION", "status": "Integrated system · Field pilot scoping", "headline": "AG-RMS. See the whole survey.", "summary": "AG-RMS combines vehicle and drone capture on a customized QGroundControl display. The profile reports <200 ms synchronization, absolute positioning accuracy ≤2 cm, elevation ≤3 cm and operation over an encrypted in-vehicle LAN.", "buyer": "Survey operators, geospatial teams and integration partners working on civilian infrastructure and industrial sites.", "inputs": [ "Capture objectives and coverage acceptance criteria", "Sensor stack, timestamps and data formats", "Field workflow, connectivity and reference measurements" ], "outputs": [ "An AG-RMS configuration and field integration scope", "A common air–ground capture display and targeted recapture workflow", "Agreed mapping data handoff, coverage assessment and accuracy checks" ], "boundary": "The figures above are reported in the supplied profile. Measurement conditions and full benchmark records are not supplied. Synchronization, positioning accuracy, cloud quality and coverage need separate acceptance criteria.", "questions": [ [ "What is AG-RMS?", "AG-RMS is an air–ground real-time mapping system built on customized QGroundControl, integrating vehicle and drone information with a proprietary real-time data bridge and multi-vehicle communications middleware." ], [ "What numerical performance does the profile report?", "It reports <200 ms synchronization, absolute positioning accuracy ≤2 cm, elevation ≤3 cm, point-cloud density >2000 points/m² and air–ground work rate >20 km/day. Full measurement conditions are not provided." ], [ "Does AG-RMS need internet connectivity?", "The described field workflow runs over an encrypted in-vehicle LAN without internet access. Network and hardware compatibility are defined for the project configuration." ], [ "How should the coverage and productivity claims be interpreted?", "The profile states 100% single-pass coverage, a 10× comparison figure and 80% shorter duration. It does not define the coverage denominator or comparative baseline; these are separate reported claims to evaluate in a scoped field trial." ] ], "cta": "Plan a mapping pilot", "related": "mapping-coverage-checklist", "sourceSection": 8, "pages": [ 12 ], "overview": "AG-RMS brings ground-vehicle and aerial viewpoints into one mapping workflow. A proprietary real-time data bridge and multi-vehicle communications middleware synchronize positions, attitudes and trajectories on the vehicle’s QGroundControl display. Operators review occluded areas and coverage gaps while still on site, then assign targeted aerial recapture.", "metrics": [ { "value": "<200 ms", "label": "Synchronization delay", "context": "Profile-reported synchronization of air and ground information on the shared display." }, { "value": "≤2 cm", "label": "Absolute positioning accuracy", "context": "Source label: absolute positioning accuracy. Elevation is listed separately at ≤3 cm." }, { "value": ">2000", "label": "Points per m²", "context": "Reported point-cloud density for the described AG-RMS configuration." }, { "value": ">20 km/day", "label": "Air–ground work rate", "context": "Reported linear survey work rate; actual route and capture conditions are not specified." }, { "value": "≤3 cm", "label": "Elevation accuracy", "context": "Separate source-listed elevation figure; validate against independent survey control." }, { "value": "100%", "label": "Single-pass coverage claim", "context": "Source-stated workflow claim. Required surfaces and trial denominator are not defined, so this is not a universal coverage guarantee." }, { "value": "10×", "label": "Comparison with traditional surveying", "context": "Reported comparison figure; the baseline and calculation method are not provided." }, { "value": "80%", "label": "Shorter project duration", "context": "Separately reported duration reduction. Do not treat this and the 10× figure as mathematically equivalent." } ], "features": [ { "title": "A common operational picture", "text": "Display vehicle trajectories and drone positions and attitudes together, giving the operator a view of captured areas and places needing another viewpoint.", "type": "Synchronized field workflow" }, { "title": "Target gaps before demobilization", "text": "Use aerial capture to complement ground-level blind areas. The intended value is to discover and fill omissions during the same mobilization.", "type": "Coverage review" }, { "title": "Work over the local vehicle network", "text": "The described configuration transfers data through an encrypted in-vehicle LAN and does not require an internet connection for that workflow.", "type": "Offline operation" } ], "tables": [ { "title": "Reference hardware and software configuration", "headers": [ "Component", "Model / platform", "Source-listed role or rating" ], "rows": [ [ "LiDAR", "RIEGL VUX-1HA-22", "1.8 million points/s" ], [ "Position and orientation", "Trimble AP+60 Land", "Positioning and attitude reference" ], [ "Panoramic imaging", "FLIR Ladybug 5+", "30 megapixels" ], [ "Aerial platform", "DJI M350 RTK", "Aerial capture platform" ], [ "Common display", "Customized QGroundControl", "Air–ground positions, attitudes and trajectories" ], [ "Data and communications", "Proprietary real-time bridge + multi-vehicle middleware", "Synchronized field workflow over an encrypted in-vehicle LAN" ] ], "note": "This is the configuration identified in the supplied profile, not a claim of universal sensor compatibility." } ], "scenarios": [ { "title": "Smart cities and real-scene 3D", "text": "Combine ground and aerial viewpoints for urban 3D reconstruction, reviewing occluded surfaces before the crew leaves.", "type": "Application context" }, { "title": "Road asset inventory and maintenance", "text": "Capture road corridors and surrounding assets with a common coverage view, then target areas missed by the vehicle.", "type": "Application context" }, { "title": "Power and energy corridors", "text": "Coordinate complementary observations of corridor assets and surrounding ground features for inspection and mapping.", "type": "Application context" }, { "title": "Emergency assessment", "text": "Support civilian mapping and coverage review when public internet connectivity is unavailable.", "type": "Application context" } ], "maturity": "The profile presents AG-RMS as a core integrated product and illustrates a vehicle platform and 3D reconstruction results. Project deployment starts with sensor compatibility, route scope and field acceptance criteria." }, { "slug": "visual-positioning", "brand": "aervimo", "title": "ZhiTu VisNav map-referenced visual navigation", "label": "ZhiTu VisNav", "category": "POSITIONING RESEARCH", "status": "Campus-tested prototype · Engineering migration", "headline": "ZhiTu VisNav. Position from what you see.", "summary": "Map-referenced visual navigation using a down-facing camera and edge compute. A reported 740-frame campus trial achieved a median position deviation of 9.8 m at 120 m altitude, with an unoptimized prototype time of 0.23 s/frame.", "buyer": "Civilian OEM navigation and autonomy teams with controlled engineering evaluation capabilities.", "inputs": [ "Civilian use case and operating domain", "Reference-map quality, age and image conditions", "Independent ground truth and integration constraints" ], "outputs": [ "A scoped research evaluation plan", "Error distributions and failure-case review requirements", "An engineering integration and maturity assessment" ], "boundary": "All reported accuracy, timing and recovery figures refer to the described prototype test. They do not establish all-weather operation, unlimited drift-free performance, precision landing or deployment readiness.", "questions": [ [ "What was measured in the campus trial?", "The supplied profile reports 740 consecutive localization frames over a roughly 1300 × 1300 m campus reference area, 9.8 m median position deviation at 120 m altitude and 0.23 s/frame for the unoptimized prototype." ], [ "Was GPS used throughout the position estimate?", "The profile states that GPS was used for the first frame and not for subsequent position estimates. It separately shows GPS ground truth for comparison with visual estimates." ], [ "What do the 100% figures refer to?", "They refer to reacquisition after leaving the map and rejection of false matches in the described sequence. Event counts and wider operating-domain statistics are not supplied, so the figures are not universal guarantees." ], [ "Is the embedded version already released?", "The profile describes migration to Rockchip RK3588, NPU quantization, C++ engineering and interface integration as ongoing. The offering is a prototype evaluation rather than a claimed released production SDK." ] ], "cta": "Discuss a civilian OEM evaluation", "related": "visual-positioning-evaluation", "sourceSection": 9, "pages": [ 13 ], "overview": "ZhiTu VisNav compares onboard camera imagery with a preloaded satellite or aerial reference map to produce an absolute visual position observation. The concept combines inertial information, learned reference-image matching and a quality gate. The profile describes a real campus trial and ongoing migration to an embedded platform.", "metrics": [ { "value": "9.8 m", "label": "Median position deviation", "context": "Reported at 120 m altitude in the campus trial; this is a median, not a maximum-error bound." }, { "value": "740", "label": "Consecutive localization frames", "context": "Profile-reported closed-loop sequence; not 740 independent flights." }, { "value": "0.23 s", "label": "Processing time per frame", "context": "Unoptimized prototype measurement; not a released embedded-platform latency specification." }, { "value": "~1300 × 1300 m", "label": "Reference-map area", "context": "Campus environment used for the reported evaluation." }, { "value": "100%", "label": "Reported reacquisition after map exit", "context": "Observed in the described sequence, with no loss or divergence reported; the number of reacquisition events is not given." }, { "value": "430 m", "label": "Largest cited false-position error", "context": "The quality gate reportedly rejected high-confidence false matches up to this deviation." }, { "value": "100%", "label": "Reported false-match rejection", "context": "Reported for the described test; the number of false-match events and broader operating-domain statistics are not provided." } ], "features": [ { "title": "Camera and reference map", "text": "A down-facing camera and edge-computing board use a preloaded satellite or aerial map as a reference for absolute visual positioning.", "type": "System concept" }, { "title": "Complementary information sources", "text": "The public architecture combines inertial information, reference-image matching and quality checks to assess whether a visual position observation is usable.", "type": "System concept" }, { "title": "Quality as part of the result", "text": "The profile reports rejecting confident but incorrect position matches. Evaluation therefore includes false matches and availability alongside typical position error.", "type": "Trial evidence" } ], "tables": [ { "title": "Reported campus evaluation", "headers": [ "Item", "Condition / observation" ], "rows": [ [ "Environment", "Real campus scene with approximately 1300 × 1300 m reference map" ], [ "Altitude", "120 m for the cited median-error result" ], [ "Sequence", "740 consecutive localization frames" ], [ "Position deviation", "9.8 m median" ], [ "Prototype timing", "0.23 s/frame, unoptimized" ], [ "GPS use", "First-frame initialization; the profile states GPS was not used for subsequent position estimates" ], [ "Reference comparison", "The profile shows GPS ground truth compared with visual estimates" ], [ "Reacquisition and false matches", "100% figures are sequence-specific; event counts are not disclosed" ] ], "note": "" }, { "title": "Engineering maturity", "headers": [ "Stage", "Status described in the profile" ], "rows": [ [ "Campus validation", "Completed prototype sequence reported" ], [ "Rockchip RK3588 migration", "In progress" ], [ "NPU quantization and C++ pipeline engineering", "In progress" ], [ "Real-time platform interface integration", "In progress" ] ], "note": "" } ], "scenarios": [ { "title": "Civilian mapping-platform research", "text": "Evaluate a map-referenced position observation in controlled conditions when conventional positioning is unavailable or unreliable.", "type": "Application context" }, { "title": "OEM feasibility assessment", "text": "Review map dependence, camera conditions, latency, typical error and incorrect matches before defining an integration program.", "type": "Application context" }, { "title": "Reference-map suitability studies", "text": "Compare the intended operating environment with the reported campus scene and identify the additional evidence needed.", "type": "Application context" } ], "maturity": "This is a campus-tested prototype undergoing engineering migration to Rockchip RK3588. The profile lists NPU quantization, C++ pipeline refactoring and real-time interface integration as ongoing work, not a released production SDK." } ] }, "note": "Authored website product and service facts, not independent verification." }