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Home Research Areas Visual SLAM LDSO: Direct Sparse Odometry with Loop Closure

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research:vslam:ldso [2018/10/02 16:34]
Nikolaus Demmel
research:vslam:ldso [2018/10/06 22:25] (current)
Rui Wang
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 These constraints are fused with a co-visibility graph of relative poses extracted from DSO's sliding window optimization. These constraints are fused with a co-visibility graph of relative poses extracted from DSO's sliding window optimization.
 Our evaluation on publicly available datasets demonstrates that the modified point selection strategy retains the tracking accuracy and robustness, and the integrated pose-graph optimization significantly reduces the accumulated rotation-, translation- and scale-drift,​ resulting in an overall performance comparable to state-of-the-art feature-based systems, even without global bundle adjustment. Our evaluation on publicly available datasets demonstrates that the modified point selection strategy retains the tracking accuracy and robustness, and the integrated pose-graph optimization significantly reduces the accumulated rotation-, translation- and scale-drift,​ resulting in an overall performance comparable to state-of-the-art feature-based systems, even without global bundle adjustment.
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 +===== Code =====
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 +You can find the code for LDSO under a GPL license at https://​github.com/​tum-vision/​LDSO. It is based on [[research:​vslam:​dso|DSO]].
  
  
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 </​bibtex>​ </​bibtex>​
  
-===== Code ===== 
- 
-You can find the code for LDSO under a GPL license at https://​github.com/​tum-vision/​LDSO. It is based on [[research:​vslam:​dso|DSO]]. 

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