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

TL;DR

MSC-VO is a visual odometry method that combines point and line features with Manhattan and structural constraints to improve accuracy in low-textured, man-made environments, even when some constraints are absent.

Contribution

The paper introduces MSC-VO, a novel RGB-D visual odometry approach that integrates structural regularities and Manhattan axes estimation using line features.

Findings

Outperforms state-of-the-art visual odometry methods.

Effective in low-textured, man-made environments.

Works well even without all structural constraints.

Abstract

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.