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First of all thanks for your interesting work and alternative approach to 3DGS SLAM using RGBD.
I'm currently benchmarking GS-ICP to other NERF or 3DGS based SLAM methods. One thing i notice is even when loosing track or drifting GS-ICP is able to "relocate" or adjust its localisation estimation... supposedly when revisiting something previously seen.
I'm quite curious on what causes this "relocalization" since the pipeline differs quite a bit from traditional SLAM implementations. Unlike loop closure and/or global as well as local bundle adjustment no optimization of previous poses are done as I understand. I guess this relocalisation-ish is then solely based on the GICP between the Target Gaussians (subset from the map) and the Soruce Gaussians (from current rgbd-frames point cloud).
Is this assumption correct, and could someone (@Riboha@bogus2000) explain this a little?
The text was updated successfully, but these errors were encountered:
Hi,
First of all thanks for your interesting work and alternative approach to 3DGS SLAM using RGBD.
I'm currently benchmarking GS-ICP to other NERF or 3DGS based SLAM methods. One thing i notice is even when loosing track or drifting GS-ICP is able to "relocate" or adjust its localisation estimation... supposedly when revisiting something previously seen.
I'm quite curious on what causes this "relocalization" since the pipeline differs quite a bit from traditional SLAM implementations. Unlike loop closure and/or global as well as local bundle adjustment no optimization of previous poses are done as I understand. I guess this relocalisation-ish is then solely based on the GICP between the Target Gaussians (subset from the map) and the Soruce Gaussians (from current rgbd-frames point cloud).
Is this assumption correct, and could someone (@Riboha @bogus2000) explain this a little?
The text was updated successfully, but these errors were encountered: