Visual Odometry with Drift-Free Rotation Estimation Using Indoor Scene Regularities

Kim, Pyojin; Coltin, Brian; Kim, Hyoun Jin

doi:10.5244/c.31.62

Cited by 32 publications

(24 citation statements)

References 19 publications

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“…The translational part is computed through three simple 1D density alignments. In [9], the translation estimation is improved through a Kanade-Lucas-Tomasi (KLT) feature tracker. However, these two approaches require the existence of multiple orthogonal planes per frame.…”

Section: Related Workmentioning

confidence: 99%

MSC-VO: Exploiting Manhattan and Structural Constraints for Visual Odometry

Company-Corcoles¹,

Garcia-Fidalgo²,

Ortíz³

2021

Preprint

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Visual odometry algorithms tend to degrade when facing low-textured scenes -from e.g. human-made environments-, where it is often difficult to find a sufficient number of point features. Alternative geometrical visual cues, such as lines, which can often be found within these scenarios, can become particularly useful. Moreover, these scenarios typically present structural regularities, such as parallelism or orthogonality, and hold the Manhattan World assumption. Under these premises, in this work, we introduce MSC-VO, an RGB-D -based visual odometry approach that combines both point and line features and leverages, if exist, those structural regularities and the Manhattan axes of the scene. Within our approach, these structural constraints are initially used to estimate accurately the 3D position of the extracted lines. These constraints are also combined next with the estimated Manhattan axes and the reprojection errors of points and lines to refine the camera pose by means of local map optimization. Such a combination enables our approach to operate even in the absence of the aforementioned constraints, allowing the method to work for a wider variety of scenarios. Furthermore, we propose a novel multi-view Manhattan axes estimation procedure that mainly relies on line features. MSC-VO is assessed using several public datasets, outperforming other state-of-the-art solutions, and comparing favourably even with some SLAM methods.

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Section: Related Workmentioning

confidence: 99%

MSC-VO: Exploiting Manhattan and Structural Constraints for Visual Odometry

Company-Corcoles¹,

Garcia-Fidalgo²,

Ortíz³

2021

Preprint

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show abstract

“…Zhou et al [23] developed a mean-shift paradigm to extract and track planar modes in surface-normal vector distribution on the unit sphere, and achieved drift-free behavior by registering the bundle of planar modes. In the work of Kim et al [24], orthogonal planar structures were exploited and tracked with an efficient SO(3)-constrained mean-shift algorithm to estimate drift-free rotation. These surface-normal-based methods can provide stable and accurate rotation estimation if the number of observed orthogonal planes is not less than two.…”

Section: Related Workmentioning

confidence: 99%

Robust Visual Compass Using Hybrid Features for Indoor Environments

et al. 2019

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Orientation estimation is a crucial part of robotics tasks such as motion control, autonomous navigation, and 3D mapping. In this paper, we propose a robust visual-based method to estimate robots’ drift-free orientation with RGB-D cameras. First, we detect and track hybrid features (i.e., plane, line, and point) from color and depth images, which provides reliable constraints even in uncharacteristic environments with low texture or no consistent lines. Then, we construct a cost function based on these features and, by minimizing this function, we obtain the accurate rotation matrix of each captured frame with respect to its reference keyframe. Furthermore, we present a vanishing direction-estimation method to extract the Manhattan World (MW) axes; by aligning the current MW axes with the global MW axes, we refine the aforementioned rotation matrix of each keyframe and achieve drift-free orientation. Experiments on public RGB-D datasets demonstrate the robustness and accuracy of the proposed algorithm for orientation estimation. In addition, we have applied our proposed visual compass to pose estimation, and the evaluation on public sequences shows improved accuracy.

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“…Manhattan World (MW) assumption is the predominant rule, thus Manhattan frame estimation is well researched for both RGB [16,30] and RGB-D images [6,12]. MW assumption serves as a guidance in many applications such as layout estimation [16,30,8,3,29,40], camera pose estimation [33,13] and reconstruction refinement [7,9].…”

Section: Related Workmentioning

confidence: 99%

Floorplan-Jigsaw: Jointly Estimating Scene Layout and Aligning Partial Scans

Cheng

Wang

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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We present a novel approach to align partial 3D reconstructions which may not have substantial overlap. Using floorplan priors, our method jointly predicts a room layout and estimates the transformations from a set of partial 3D data. Unlike the existing methods relying on feature descriptors to establish correspondences, we exploit the 3D "box" structure of a typical room layout that meets the Manhattan World property. We first estimate a local layout for each partial scan separately and then combine these local layouts to form a globally aligned layout with loop closure. Without the requirement of feature matching, the proposed method enables some novel applications ranging from large or featureless scene reconstruction and modeling from sparse input. We validate our method quantitatively and qualitatively on real and synthetic scenes of various sizes and complexities. The evaluations and comparisons show superior effectiveness and accuracy of our method.

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Visual Odometry with Drift-Free Rotation Estimation Using Indoor Scene Regularities

Cited by 32 publications

References 19 publications

MSC-VO: Exploiting Manhattan and Structural Constraints for Visual Odometry

MSC-VO: Exploiting Manhattan and Structural Constraints for Visual Odometry

Robust Visual Compass Using Hybrid Features for Indoor Environments

Floorplan-Jigsaw: Jointly Estimating Scene Layout and Aligning Partial Scans

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