LDSO: Direct Sparse Odometry with Loop Closure

In this paper we present an extension of Direct Sparse Odometry (DSO) [1] to a monocular visual SLAM system with loop closure detection and pose-graph optimization (LDSO). As a direct technique, DSO can utilize any image pixel with sufficient intensity gradient, which makes it robust even in featureless areas. LDSO retains this robustness, while at the same time ensuring repeatability of some of these points by favoring corner features in the tracking frontend. This repeatability allows to reliably detect loop closure candidates with a conventional feature-based bag-of-words (BoW) approach. Loop closure candidates are verified geometrically and Sim(3) relative pose constraints are estimated by jointly minimizing 2D and 3D geometric error terms. 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.

Authors

• Xiang Gao 0006
• Rui Wang 0037
• Nikolaus Demmel
• Daniel Cremers

Keywords

• Optimization
• Feature extraction
• Microsoft Windows
• Simultaneous localization and mapping
• Cameras
• Bundle adjustment
• Robustness
• Loop Closure
• Direct Sparse Odometry
• Optimal Window
• Related Constraints
• Relative Pose
• Global Adjustment
• Global Scale
• Global Optimization
• Indirect Method
• Direct Approach
• Feature Points
• Global Map
• Current Function
• Inertial Measurement Unit
• 3D Point
• Feature Matching
• Pose Estimation
• Direct Alignment
• Visual Odometry
• Feature-based Methods
• Current Window
• Global Graph
• Reprojection Error
• Points In Window
• Loop Detection
• Feature Extraction Step