LDSO: Direct Sparse Odometry with Loop Closure

By Xiang Gao, Rui Wang, Nikolaus Demmel, Daniel Cremers. IEEE/RJS International Conference on Intelligent RObots and Systems, 2018

319 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

View Paper: https://www.semanticscholar.org/paper/dd5bdbbab7077764bdc70bf24d74ed50a38d2efc

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Citations in the corpus

BAD SLAM: Bundle Adjusted Direct RGB-D SLAM (2019, methodology): “DSO has been extended with loop closure handling, but only using pose graph optimization without BA [20].”
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM (2020, methodology): “Extensions of this work include stereo [29], loop-closing using features and DBoW2 [59] [60], and visual-inertial odometry [46].”
Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection (2020, methodology): “They are widely used in visual SLAM such as ORB SLAM [11] and LDSO [12].”