Closed ZhouXiner closed 2 years ago
How is it worse? Only changes in code compared to ORB-SLAM2 are :
ORB_SLAM3
CV_LOAD_IMAGE
And the difference in the Slam system is added imu support. I don't think they changed the way how no Imu systems work.
Thanks for reply. The APE is bigger in TUM RGBD dataset like fr1_desk2 and fr1_room. Actually, the loop closuing part also changed a lot. Maybe I should compare the preformance without loop.
I am experiencing the same issue with the monocular pipeline; the accuracy is worse than ORB-SLAM2.
I've tested ORB-SLAM2 and ORB-SLAM3 on KITTI dataset in RGBD mode with my deep learning model predicted depth. It turns out that ORB-SLAM3 performs worse than ORB-SLAM2 and have scale drift issues.
Hi, @ZhouXiner @melhashash Try this solution #107. It seems the RGBD mode didn't set mbf value correctly.
Hi, @ZhouXiner @melhashash Try this solution #107. It seems the RGBD mode didn't set mbf value correctly.
Thanks, I will try it.
Hi, @ZhouXiner @melhashash Try this solution #107. It seems the RGBD mode didn't set mbf value correctly.
Thanks for your suggestion, but I am talking about the monocular case.
Could we add depth sensor error as parameter(i.e noise parameter in IMU) to optimization in RGB-D SLAM? If so, how can we add it?
@ZhouXiner can you give me the link to dataset and the command? I tried on freiburg desk dataset but it doesn't work for me.
Version v1.0 has corrected some bugs in RGB-D SLAM
I try to run the RGBD type in TUM RGBD Dataset, but the performance is even worse than ORBSLAM2 in some sequence like fr1_desk2. Has anyone struggled the same problem like me?