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This paper aims to select features that contribute most to the pose estimation in VO/VSLAM. Unlike existing feature selection works that are focused on efficiency only, our method significantly improves the accuracy of pose tracking, while introducing little overhead. By studying the impact of feature selection towards least squares pose optimization, we demonstrate the applicability of improving accuracy via good feature selection. To that end, we introduce the Max-logDet metric to guide the feature selection, which is connected to the conditioning of least squares pose optimization problem. We then describe an efficient algorithm for approximately solving the NP-hard Max-logDet problem. Integrating MaxlogDet feature selection into a state-of-the-art visual SLAM system leads to accuracy improvements with low overhead, as demonstrated via evaluation on a public benchmark.
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Good Line Cutting: Towards Accurate Pose Tracking of Line-Assisted VO/VSLAM
This paper tackles a problem in line-assisted VO/VSLAM: accurately solving the least squares pose optimization with unreliable 3D line input. The solution we present is good line cutting, which extracts the most-informative sub-segment from each 3D line for use within the pose optimization formulation. By studying the impact of line cutting towards the information gain of pose estimation in line-based least squares problem, we demonstrate the applicability of improving pose estimation accuracy with good line cutting. To that end, we describe an efficient algorithm that approximately approaches the joint optimization problem of good line cutting. The proposed algorithm is integrated into a state-of-the-art line-assisted VSLAM system. When evaluated in two target scenarios of line-assisted VO/VSLAM, low-texture and motion blur, the accuracy of pose tracking is improved, while the robustness is preserved.
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- PAR ID:
- 10111562
- Date Published:
- Journal Name:
- European Conference on Computer Vision
- Page Range / eLocation ID:
- 527-543
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-01216-8_32
