[GLIM] NTU VIRAL (sbs_03)

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  • Опубликовано: 2 июл 2024
  • (Coming soon) github.com/koide3/glim
    / k_koide3

Комментарии • 5

  • @Noahitis
    @Noahitis 9 дней назад

    this is sick

  • @shivavarunadicherla
    @shivavarunadicherla 10 дней назад +2

    Great work. I'm guessing you are fusing the results of IMU's estimate of position and SFM data from camera. How much drift are you getting as of now?

    • @kenjikoide6076
      @kenjikoide6076  7 дней назад

      It fuses all Visual-LiDAR-IMU constraints on a unified factor graph. Because it was an easy setup for the LiDAR, it got almost no drift at all.

    • @shivavarunadicherla
      @shivavarunadicherla 7 дней назад

      ​@@kenjikoide6076 If LiDAR's also participating in Motion estimation, it sure would be very accurate. I would bet that if you used only the data from LiDAR you would probably get the same estimated motion. I was thinking that LiDAR was used as ground truth.

    • @kenjikoide6076
      @kenjikoide6076  7 дней назад

      @@shivavarunadicherla Yes, visual constraints brought only minor accuracy gain in this dataset indeed. This is just a demonstration. What we are truly aiming for is overcoming situations where point clouds become completely degenerate (e.g., tunnels), and we've confirmed that visual constraints greatly improves the reliability in such situations.