RRepoGEO

REPOGEO REPORT · LITE

i2Nav-WHU/KF-GINS

Default branch main · commit f39bc2e7 · scanned 5/25/2026, 5:23:14 AM

GitHub: 1,141 stars · 299 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface i2Nav-WHU/KF-GINS, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    gnss, ins, kalman-filter, ekf, sensor-fusion, navigation, c-plus-plus, imu, integrated-navigation, error-state-kalman-filter, loosely-coupled
  • highreadme#2
    Correct AI's misunderstanding of coupling type and architecture in README

    Why:

    CURRENT
    A GNSS/INS loosely-coupled integrated navigation algorithm based on the extended Kalman filter architecture (error state vector), including IMU error compensation, inertial navigation solution, Kalman filter, error feedback, etc.
    COPY-PASTE FIX
    A GNSS/INS loosely-coupled integrated navigation algorithm based on the extended Kalman filter (EKF) architecture (error state vector). This system specifically implements a classical EKF approach for sensor fusion, *not* a tightly coupled system or one based on factor graph optimization. It includes IMU error compensation, inertial navigation solution, Kalman filter, and error feedback.
  • mediumabout#3
    Enhance the repository description for more specificity

    Why:

    CURRENT
    An EKF-Based GNSS/INS Integrated Navigation System
    COPY-PASTE FIX
    A C++ EKF-Based Error-State GNSS/INS Integrated Navigation System

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface i2Nav-WHU/KF-GINS
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
RTKLIB
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. RTKLIB · recommended 1×
  2. ROS (Robot Operating System) · recommended 1×
  3. robot_localization package · recommended 1×
  4. filterpy library · recommended 1×
  5. MATLAB/Simulink · recommended 1×
  • CATEGORY QUERY
    How to integrate GNSS and IMU data for navigation using an Extended Kalman Filter?
    you: not recommended
    AI recommended (in order):
    1. RTKLIB
    2. ROS (Robot Operating System)
    3. robot_localization package
    4. filterpy library
    5. MATLAB/Simulink
    6. Navigation Toolbox
    7. PX4 Autopilot
    8. ArduPilot
    9. OpenCV (Computer Vision Library)

    AI recommended 9 alternatives but never named i2Nav-WHU/KF-GINS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What C++ library provides an error-state Kalman filter for sensor fusion applications?
    you: not recommended
    AI recommended (in order):
    1. Kalman Filter for C++ (KF4C++)
    2. Robot Operating System (ROS) - `robot_localization` package
    3. `kalman-filter` by jrl-umi3218 (jrl-umi3218/kalman-filter)
    4. PCL (Point Cloud Library) - `pcl::tracking` module
    5. OpenCV - `cv::KalmanFilter`

    AI recommended 5 alternatives but never named i2Nav-WHU/KF-GINS. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of i2Nav-WHU/KF-GINS?
    pass
    AI named i2Nav-WHU/KF-GINS explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts i2Nav-WHU/KF-GINS in production, what risks or prerequisites should they evaluate first?
    pass
    AI named i2Nav-WHU/KF-GINS explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo i2Nav-WHU/KF-GINS solve, and who is the primary audience?
    pass
    AI named i2Nav-WHU/KF-GINS explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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i2Nav-WHU/KF-GINS — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite