Context Preview: Interestingly enough, decreasing the number of features significantly improves ATE from 8.0 to 0.033 on this SDVL (Semi-Direct Visual Localization) is an efficient SLAM algorithm developed at Rey Juan Carlos University (Spain).
Stella Vslam Example Demo With Tum Rgbd Dataset - Information Summary
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Information Summary
SDVL (Semi-Direct Visual Localization) is an efficient SLAM algorithm developed at Rey Juan Carlos University (Spain). Interestingly enough, decreasing the number of features significantly improves ATE from 8.0 to 0.033 on this
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- Interestingly enough, decreasing the number of features significantly improves ATE from 8.0 to 0.033 on this
- SDVL (Semi-Direct Visual Localization) is an efficient SLAM algorithm developed at Rey Juan Carlos University (Spain).
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