@@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.
@@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.
![[ICRA2024] MegaParticles : 6-DoF Monte Carlo Localization with One Million Particles](http://i.ytimg.com/vi/8FQDdUwzZJE/mqdefault.jpg)
![[GLIM] Mapping with Azure Kinect](/img/n.gif)
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?
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.
@@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.
@@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.
this is sick
where is dataset?